Find research datasets worth reusing

opencc-zeroMar 2020View details →

IODP Expedition 366 Navigation

Operational navigation data were measured using Trimble GPS systems and saved as navigational data files including configuration, data, logs, pictures, vehicle, and waypoints. Site Fix summary data and plots are presented in Microsoft Excel. Data are presented by expedition.

openmit-licenseMay 2016View details →

Datasets and config files for the Chemical Navigator tests

This are the datasets and config files used to test the MSc in Bioinformatics thesis at the UPF by Josep Arus-Pous. This dataset includes: <ul> <li>Configuration files in HOCON format that were used for the tests (n = 250, 500 and 5000).</li> <li>Raw data file of the results of the tests, both in csv and html.</li> <li>A list of the 200 pairs of molecules selected from ZINC and used for the navigation test.</li> </ul>

opencc-by-4.0Jun 2022View details →

Alternation emerges as a multi-modal strategy for turbulent odor navigation - Dataset

Dataset from 3D direct numerical simulation of odor evolution in a turbulent channel flow. The dataset contains two .mat files with nose (z ~ 50 cm) and ground (z = 0) level 2D slices; coordinates.mat with the coordinates in X and Y directions; and a Jupyter Notebook file (Read_nose_ground_dataset) to read and plot the 2D fields. Updated on May 20th 2025 3D velocity data have been added to the dataset.

Bodily and Visual-Cognitive Navigation Aids to Enhance Spatial Recall in Mild Cognitive Impairment

Individuals with mild cognitive impairment (MCI) syndrome often report navigation difficulties, accompanied by impairments in egocentric and allocentric spatial memory. However, studies have shown that both bodily cues (e.g., motor commands, proprioception, vestibular information) and visual-cognitive cues (e.g., maps, directional arrows, attentional markers) can support spatial memory in MCI. These aids offer valuable insights for designing navigation training programs in aging. Fifteen MCI patients were recruited for this study. Their egocentric and allocentric memory recall performances were tested through a navigation task with five different virtual reality (VR) assistive encoding procedures (bodily, vision only, interactive allocentric map, reduced executive load, free navigation without cues). Bodily condition consisted of an immersive VR setup to engage self-motion cues, vision only condition consisted of passive navigation without interaction, in the interactive allocentric map condition patients could use a bird-view map, in the reduced executive load condition directional cues and attentional markers were employed, and during free navigation no aid was implemented. Bodily condition improved spatial memory compared to vision only and free navigation without cues. In addition, the interactive allocentric map was superior to the free navigation without cues. Surprisingly, the reduced executive load was comparable to vison only condition. Moreover, a detrimental impact of free navigation was observed on allocentric memory across testing trials. These findings challenge the notion of an amodal representation of space in aging, suggesting that spatial maps can be affected by the modality in which the environment was originally encoded.

opencc-by-4.0Dec 2023View details →

Input geophysical and geological data for "Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees"

This is a companion dataset to the manuscript:  Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees,by: Jeremie Giraud , Mary Ford, Guillaume Caumon, Lachlan Grose, Vitaliy Ogarko, Roland Martin, and Paul Cupillard. This dataset contains the input data used in the inversion, in terms of the gravity data and the geological data used in the inversion. The *.txt file contains the gravity data as inverted in the manuscript: X, Y, Z, Value. The *.csv file contains the geological data: location of the contacts and orientation data.

F I G U R E 5 in Migration patterns and navigation cues of Atlantic salmon post-smolts migrating from 12 rivers through the coastal zones around the Irish Sea

F I G U R E 5 Rose diagrams depicting (a) the hour of the day and (b) the direction of currents () when Atlantic salmon (Salmo salar) post-smolts were initially detected at a unique acoustic receiver on monitoring line B. The green and blue arrows show the mean hour (a) and mean current direction (b) when post-smolts were initially detected respectively (Lilly et al., 2022). The orange and yellow bands (a) show the variation in sunrise and sunset times for the total period over which any post-smolts were detected on monitoring line B (ie. April 21st–June 20th).

F I G U R E 4 in Migration patterns and navigation cues of Atlantic salmon post-smolts migrating from 12 rivers through the coastal zones around the Irish Sea

F I G U R E 4 Heatmaps displaying the number of Atlantic salmon (Salmo salar) post-smolts detected at each acoustic receiver on monitoring lines A and B (Figure 1) during the period of this study. The black stars show the location of each river (n = 11) where Atlantic salmon post-smolts originated. Rivers are grouped by coastal region where they entered the Irish Sea (Figure 1, (a) Region 1: Rivers Derwent, Nith, Bladnoch; (b) Region 2: Rivers Endrick, Gryffe; (c) Region 3: Rivers Bann, Bush, Carey, Glendun; (d) Region 4: Rivers Roe, Faughan).

F I G U R E 2 A in Migration patterns and navigation cues of Atlantic salmon post-smolts migrating from 12 rivers through the coastal zones around the Irish Sea

F I G U R E 2 A boxplot plot displaying the dates (mm-dd) when Atlantic salmon (Salmo salar) post-smolts (n = 582) were last detected in their natal river/estuary (Rivers Endrick, Gryffe, Roe, Faughan) or coastal embayment (River Burrishoole) and entered the coastal zones of the Irish Sea or the west coast of Ireland (River Burrishoole; Figure 1: Clew Bay) and were detected on monitoring lines A and B (excluding the River Burrishoole Figure 1). In the boxplots, the centre line represents the median, the box encompasses the 25 to 75% quartiles, the bars are the values within 1.5 interquartile units and the dots represent outliers. It should be noted that the dates when smolts were tagged (represented by the dashed black line) differed in each river system. The thick black lines divide rivers into their coastal regions (see methods).

F I G U R E 1 Map displaying the 14 in Migration patterns and navigation cues of Atlantic salmon post-smolts migrating from 12 rivers through the coastal zones around the Irish Sea

F I G U R E 1 Map displaying the 14 capture sites in 12 rivers where Atlantic salmon smolts (n = 1008) were captured for tagging in England, Scotland, Northern Ireland and the Republic of Ireland for this study. In addition, 60 hatchery origin smolts were tagged and released in the River Burrishoole. The coastal region each river belongs to is referenced in brackets next to the river name. Where Region one (1) refers to the Solway Firth (Rivers Derwent, Nith, Bladnoch); Region two (2) refers to the Clyde Estuary (Rivers Endrick and Gryffe); Region three (3) refers to the Bush Coastal region (rivers Bann, Bush, Carey and Glendun); Region four (4), refers to Lough Foyle (rivers Roe and Faughan); Region five (5), refers to Clew Bay (River Burrishoole). Tagged fish release sites are represented by stars, and acoustic receivers (n = 183) are represented by gray dots. Marine monitoring lines (A and B) in the Irish Sea are labeled in alphabetical order from south to north. Twenty-two acoustic receivers were initially deployed at monitoring line A. One hundred and eight acoustic receivers were deployed at monitoring line B and are labeled in numerical order from the furthest west receiver (R1) on the monitoring line to the furthest east (R108). Refer to Figure S2 for the locations of acoustic receivers that were not retrieved from marine monitoring line A (n = 2) and B (n = 9).

F I G U R E 3 in Migration patterns and navigation cues of Atlantic salmon post-smolts migrating from 12 rivers through the coastal zones around the Irish Sea

F I G U R E 3 The binomial General Linear Model (GLM) model showing the effect of minimum migration distance (Distance [km]) from the exit of smolts natal river/estuary to monitoring line B on the probability of migration success (measured as minimum migration success) of Atlantic salmon (Salmo salar) post-smolt through the Irish Sea. The shaded region is the 95% confidence interval of the final model.

opencc-by-4.0Mar 2022View details →

Supplemental material: Operative videos on application of microscope-based augmented reality with intraoperative computed tomography-based navigation for resection of skull base meningiomas

Supplemental material Operative videos: Patient number 9: Microsurgical resection of medial sphenoid wing meningioma using microscope-based augmented reality and intraoperative computed tomography-based navigation Pt 28:Microsurgical resection of right clinoidal meningioma via fronto-temporal craniotomy with microscope-based augmented reality Pt 31:Microsurgical resection of recurrent sphenoid wing meningioma using microscope-based augmented reality with intraoperative computed tomography Pt 36: Microsurgical resection of giant olfactory meningioma via bifrontal approach with use of augmented reality and intraoperative CT-based navigation

opencc-by-4.0Apr 2022View details →

Data from: Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity

Short description This repository contains the relevant data and code used for the analyses of the scientific publication: "Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity", published in the Journal of Applied Ecology. For further details please see the original article and its supplementary materials.   Organization of the data The repository contains two main folders:    1. Target indicators & spatial analysis ‘target indicators.csv’: Measured variables that have been quantified at the CROSSLINK field sampling campaign in the Zwalm catchment (EPT taxa richness, diatoms functional evenness, cotton-strip assay). ‘bio-suitability segments.csv’: Biophysical suitability for food production of the arable land for each riparian segment of the Zwalm. ‘spatial analysis.xlsx’: Results of the Zwalm spatial analyses addressing land-use and physiographic properties of the (1) local riparian corridors; (2) full riparian corridors within in the upstream catchments and (3) total upstream catchment areas for each sampling site. ‘Summary model development Zwalm.pptx’: Additional information on the models that have been used in the CoMOLA optimization framework.    2. CoMOLA input & parameterisation The files in this folder can be used for the parameterisation of the Python tool CoMOLA (Strauch et al., 2019). Source for CoMOLA, including user manual: https://github.com/michstrauch/CoMOLA ‘config.ini’: Basic configuration file of CoMOLA (needs to be adjusted to local settings) ‘input’ folder: Includes the CoMOLA input files that have been used in our study. See CoMOLA manual for more details on each file. ‘models’ folder: Includes the Python code of the models that are used for the calculation of all target indicators within the optimization framework (‘Zwalm_4_Models_v1_utf8.py’). The sub-folders ‘GIS_temp_files’ and ‘Input’ contain all files that are needed and have been used to run the Python code.

opencc-by-4.0Apr 2022View details →

2D Sound Navigation - Tutorial Materials

Materials presented to the experiment participants to familiarize them with the navigation controls and auditory guidance.

opencc-by-4.0Jul 2022View details →

A Priority Map for Vision-Language Navigation - Datasets

This archive contains full versions of the datasets and additional data presented in the following paper: A Priority Map for Vision-and-Language Navigation with Trajectory Plans and Feature-Location Cues A priority map module (PM-VLN) boosts the performance of transformer-based architectures in navigation tasks by combining temporal sequence alignment and feature-level localisation in cross-modal inputs. The module is pretrained on trajectory estimation and a multi-objective task that pairs location estimation with cross-modal sentence prediction. Two datasets are introduced for the auxiliary tasks:  - TR-NY-PIT-central - a set of path traces for routes in two urban locations.  - MC-10 - a set of samples with multimodal inputs representing landmarks in 10 US cities. Full details and links for this research are available at the following link: https://jasonarmitage-res.github.io/projects/priority_map/ Additional data comprising path traces for routes in Manhattan and language tokens for the Touchdown task are provided for training and evaluating the PM-VLN and framework on the Touchdown benchmark. Please refer to the following link for details on the Touchdown dataset and StreetLearn environment: https://sites.google.com/view/streetlearn/touchdown

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Carbon dioxide and blood-feeding shift visual cue tracking during navigation in Aedes aegypti mosquitoes

Hematophagous mosquitoes need a blood meal to complete their reproductive cycle. To accomplish this, female mosquitoes seek vertebrate hosts, land on them, and bite. As their eggs mature, they shift attention away from hosts and towards finding sites to lay eggs. We asked whether females were more tuned to visual cues when a host-related signal, carbon dioxide, was present, and further examined the effect of a blood meal, which shifts behavior to ovipositing. Using a custom, tethered-flight arena that records wing stroke changes while displaying visual cues, we found the presence of CO2 enhances visual attention towards discrete stimuli and improves contrast sensitivity for host-seeking Aedes aegypti mosquitoes. Conversely, intake of a blood meal reverses vertical bar tracking, a stimulus that non-fed females readily follow. This switch in behavior suggests that physiological status modulates visual attention in mosquitoes, a phenomenon that has been described before in olfaction but not in visually-driven behaviors.

opencc-zeroAug 2022View details →

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Collective navigation as a solution to noisy navigation and its vulnerability to population loss

Many animals use the geomagnetic field to migrate long distances with high accuracy; however, research has shown that individual responses to magnetic cues can be highly variable. Thus, it has been hypothesized that magnetoreception alone is insufficient for accurate migrations, and animals must either switch to a more accurate sensory cue or integrate their magnetic sense over time. Here we suggest that magnetoreceptive migrators could also use collective navigation strategies. Using agent-based models, we compare agents utilizing collective navigation to both the use of a secondary sensory system and time-integration. Our models demonstrate that collective navigation allows for 70% success rates for noisy navigators. To reach the same success rates, a secondary sensory system must provide perfect navigation for over 73% of the migratory route, and time integration must integrate over 50 time-steps, indicating that magnetoreceptive animals could benefit from using collective navigation. Finally, we explore the impact of population loss on animals relying on collective navigation. We show that as population density decreases, a greater proportion of individuals fail to reach their destination and that a 50% population reduction can result in up to a 37% decrease in the proportion of the individuals completing their migration.

opencc-zeroOct 2022View details →

Figure 9. Trajectory Algorithm Simulation-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

We have presented the system for a fully autonomous navigation of an UAV based on Omni directional vision system and image processing. we explain vision system configuration ,image processing and feature extraction methods and finaly suggest an algorithm based on potential field for navigation of an UAV.

Figure 8. Potential at every point; it is highest in the obstacles and lowest at the goal-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

The numerical potential field path planner is guaranteed to produce a path even if the start or goal is placed in an obstacle. If there is no possible way to get from the start to the goal without passing through an obstacle then the path planner will generate a path through the obstacle, although if there is any alternative then the path will do that instead. For this reason, it is important to make sure that there is some possible path, although there are ways around this restriction such as returning an error if the potential at the start point is too high. The path is found by moving to the neighboring square with the lowest potential, starting at any point in the space and stopping when the goal is reached.

Figure 7. Obstacle force (repulsive potential) and goal force obstacle force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

Figure 6. Goal force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

Since the motion trajectory of UAV is divided into several median points that the UAV should reach them one by one in a sequence the output obtained after the execution of AI will be a set of position and velocity vectors. So the task of the trajectory will be to guide the UAV through the obstacles to reach the destination. The routine used for this purpose is the potential field method (also an alternative new method is in progress which models the UAV motion through opponents same as the owing of a bulk of water through obstacles) [5]. In this method, different electrical charges are assigned to UAV, obstacles, and the destination. Then by calculating the potential field of this system of charges a path will be suggested for the UAV. At a higher level, predictions can be used to anticipate the position of the obstacles and make better decisions in order to reach the desired vector. In our path- planning algorithm, an articial potential field is set up in the space; that is, each point in the space is assigned a scalar value. The value at the goal point is set to be 0 and the value of the potential at all other points is positive.