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479 results for “human interaction”
Data from: Human-driven breakdown of predator-prey interactions in the northern Adriatic Sea
Open the record for dataset details and reuse information.
Top-down interactions in streams draining human-modified landscapes at the Coweeta Hydrologic Laboratory from 1997 to 1998: Gradient algae data
Spatial and temporal variability of consumer-controlled forces have been the subject of much debate in ecology. To date, most studies considering variability of these top-down interactions have focused on systems minimally impacted by human activities. However, human modification of the landscape is prevalent, and it can significantly affect the strength and outcome of species interactions. The objective of this study was to examine how top-down interactions vary among streams with differing amounts of human disturbance in their watersheds. To address this issue, we experimentally excluded macroconsumers (fishes and crayfishes) from benthic areas of five southern Appalachian streams. These sites represented a range of human watershed development, from 100% to < 50% forested; macroconsumer assemblages at low development sites were dominated by benthic insectivores (Cottus bairdi) and crayfishes, whereas algivores (Campostoma anomalum) and general insectivores (e.g., Notropis leuciodus) were common at more developed sites. Using ceramic tiles as sampling substrates, we compared sediment, algal assemblages (chlorophyll a, AFDM, abundance, biovolume, and composition), and insect assemblages (abundance, biomass, and composition) in macroconsumer exclusion and control areas.
Human-Swarm Teaming with Proximal Interactions
<p>Autonomous swarms are capable of accomplishing simple tasks but when it comes to critical situations (e.g., disaster management) they cannot manage the whole mission. By establishing a human-swarm interaction we create a system that can outperform humans or autonomous swarms when working independently. We propose a human-swarm teaming with proximal interactions between human operators and robots in a multi-agent system. One of the major challenges in a human-swarm interaction is acquiring global information about the swarm's state and visualizing it to the human operators. Continuous swarm observation requires global communication between the human operator and the swarm that limits scalability and comes with a high communication cost. In our approach, we only allow one-to-one communications in local neighborhoods between agent-agent and operator-agent. By doing that, we decrease the cognitive complexity of human-swarm interaction to O(1). Agents collectively explore and map the environment by disseminating their observations and incorporating their neighboring agents' maps. Human operators then use these maps to estimate the swarm's state, manipulate the maps to control the swarm, and as a result, determine the level of autonomy of the swarm. We verify our approach with a disaster management scenario where a simulated aerial swarm is spread across a mission zone to explore an environment. Our proposed method for human-swarm teaming with proximal interactions successfully guides a swarm to explore a mission area in a dynamic environment and allows a single operator to control the swarm.</p>
Human Receptor-Interacting Serine/Threonine-Protein Kinase 2 (RIPK2); A Target Enabling Package
<p>RIPK2 inflammatory signalling downstream from the bacteria-sensing receptors NOD1 and NOD2 is associated with auto-immune and inflammatory conditions. RIPK2 inhibition has shown promise in disease models of inflammatory bowel disease and multiple sclerosis. In this TEP, we reveal a lack of correlation between inhibitor efficacy in cells and their potency using in vitro kinase assays. We show that RIPK2 kinase activity is in fact dispensable for NOD2 inflammatory signalling and that RIPK2 inhibitors function instead by antagonizing XIAP-binding and ubiquitination of RIPK2. We characterise the molecular basis for this effect. We also solved the first crystal structure of the RIPK2 kinase domain and applied a range of biochemical and cellular assays to profile type I and type II RIPK2 kinase inhibitors. Overall, our study illustrates how to target the ATP-binding pocket in RIPK2 to interfere with the RIPK2-XIAP interaction for modulation of NOD signalling.</p>
Postural Optimization for a Safe and Comfortable Human-Robot Interaction: Experiment Dataset
<p>In human-robot collaboration the robot's behavior impacts the worker's safety, comfort, and his acceptance of the robotic system. In this paper we address the problem of how to improve the worker's posture during human-robot collaboration. Using postural assessment techniques, and a personalized human kinematic model, we optimize the model body posture to fulfill a task while avoiding uncomfortable or unsafe postures. We then derive a robotic behavior that leads the worker towards that improved posture. We validate our approach in an experiment involving a joint task with 39 human subjects and a Baxter torso-humanoid robot.</p> <p>This repository contains the anonymized recorded data of our experiment. For all the subjects, we have included their recorded posture, using a motion capture system, and a video taken from a camera located on the robot head. Each data is divided by subjects and by tested conditions. In a separate file, we also include the result of the survey answered alongside the experiment.</p>
Human Interaction Image (HII) dataset
<p>The Human Interaction Image (HII) dataset is a new dataset containing Web images from Commercial Search Engines (Google, Bing and Flickr). We use keyword search to collect images corresponding to four types of interactions: handshake, highfive, hug, kiss. Then we manually filter the irrelevant images. The dataset contains 2410 images with at least 550 images per interaction.</p> <p>The dataset can be applied, but not limited to the following research areas:</p> <ul> <li>interaction recognition/prediction</li> <li>action recognition</li> <li>video analysis</li> <li>transfer learning</li> </ul> <p>Please cite the following paper if you use the HII dataset in your work (papers, articles, reports, books, software, etc):</p> <ul> <li>J. Li, Y. Wong, Q.Zhao, M. Kankanhalli<br> <strong>Attention Transfer from Web Images for Video Recognition</strong><br> <em>ACM Multimedia</em>, 2017.<br> http://doi.org/10.1145/3123266.3123432</li> </ul>
Co-occurring ripple oscillations facilitate neuronal interactions between cortical locations in humans
<h3>Contact:</h3><h3>Ilya Verzhbinsky</h3><h3>ilya@health.ucsd.edu</h3><p> </p><p>This is the processed data used to generate the results in the manuscript:</p><p>Co-occurring ripple oscillations facilitate neuronal interactions between cortical locations in humans. <i>PNAS</i> (2023).</p><p>To analyze this data, please first access the code at the following repository: <a href="https://github.com/iverzh/coripple-prediction"><strong>https://github.com/iverzh/coripple-prediction</strong></a></p><p>All downloaded zip files should be uncompressed and placed in a directory named <i>out/ </i>in the <i>CoRipplePredictionPNAS/</i> folder.</p><p> </p><p> </p><p> </p>
Causal HRSI Dataset: Human-Robot Spatial Interaction Dataset for Causal Analysis from Mobile Platforms
<h2>Causal HRSI Dataset: Human-Robot Spatial Interaction Dataset for Causal Analysis from Mobile Platforms</h2> <div>The dataset captures a Human-Robot Spatial Interaction (HRSI) scenario between a person and the TIAGo robot. It focuses specifically on human-goal and human-robot spatial interaction in an indoor environment, captured from the perspective of a 3D Velodyne VLP-16 LiDAR mounted on the TIAGo robot. It includes:</div> <ul> <li>rosbags containing: Velodyne LiDAR point clound, robot and human state (position, orientation and velocities);</li> <li>CSV files containing trajectories of the person and the robot generated by post-processing the rosbags;</li> <li>the map of the environment extracted from the TIAGo robot.</li> </ul> <p><strong>15 participants</strong> took part in the experiment, with the dataset capturing <strong>5 minutes of HRSI motion for each participant</strong>.</p> <h3>Experiment Description</h3> <p>The experiment and data collection occurred in a laboratory room of the University of Lincoln (UK), measuring 5 x 8.2m. <br>Fifteen participants (6 females, aged between 25 and 55) took part in the experiment. Seven of them were used to work with a robot. They were required to walk between four goal positions and avoid the robot if a cross occurs. A predefined rectangular path was set for the TIAGo robot to navigate along the room and generate frequent interactions with the participants.</p> <p>The experimental procedure can be described as follows. Each participant started from one of the four target positions. The next target position was randomly chosen by the participant, who then started moving towards it. Upon reaching the goal position, the participant stopped there and randomly chose the next goal, repeating the process for 5 minutes. In this experimental setting, the robot was considered by the participant as an obstacle to avoid while walking towards their target positions.</p> <h3>Directory Structure</h3> <p>Dataset<br>|<br>|____Map: folder containing the map of the environment extracted from the TIAGo robot<br>|<br>|____RosBags: forder containing the rosbag for each partipant<br>| |____A1.bag<br>| |____A2.bag<br>| |____A3.bag<br>| |____A4.bag<br>| |____A5.bag<br>| |____A6.bag<br>| |____A7.bag<br>| |____A8.bag<br>| |____A9.bag<br>| |____A10.bag<br>| |____A11.bag<br>| |____A12.bag<br>| |____A13.bag<br>| |____A14.bag<br>| |____A15.bag<br>|<br>|____Trajectories: postprocessed trajectories extracted for the rosbag files <br> |____A1_traj.csv<br> |____A2_traj.csv<br> |____A3_traj.csv<br> |____A4_traj.csv<br> |____A5_traj.csv<br> |____A6_traj.csv<br> |____A7_traj.csv<br> |____A8_traj.csv<br> |____A9_traj.csv<br> |____A10_traj.csv<br> |____A11_traj.csv<br> |____A12_traj.csv<br> |____A13_traj.csv<br> |____A14_traj.csv<br> |____A15_traj.csv</p>
IntelliMan_WP4_Adaptive Shared Autonomy_T4.2_Advanced human-robot interaction modalities_human robot handover_v0
<p><span>The dataset contains data related to the experiments presented in the publication:</span></p> <p><em><span>M. Costanzo, C. Natale and M. Selvaggio, "Visual and Haptic Cues for Human-Robot Handover*," 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Busan, Korea, Republic of, 2023, pp. 2677-2682, doi: 10.1109/RO-MAN57019.2023.10309480.</span></em></p>
Automatic Generation of Explanations in Autonomous Systems: Enhancing Human Interaction in Smart Home Environments
<p>The file named “Dataset” is the generation of scenarios and explanations used for the proposal.<br>The file named “Questionnaire answers & data analysis” corresponds to the application of a questionnaire addressed to 118 people.</p>
Insights into the DNA and RNA Interactions of Human Topoisomerase III Beta Using Molecular Dynamics Simulations
<p>hTOP3 simulations for both covalently and non-covalently bound DNA and RNA substrates. Simulation times = 300ns, with 1/ns per frame = 300 frames each.</p>
Datasets of Human-shark interactions in New Caledonia and Reunion Island (1980-2022)
<p>This dataset encompasses detailed records of human-shark interactions incidents in New Caledonia and Reunion Island from 1980 to 2022. It is designed to support a multi-criteria analysis of these incidents, providing insights into the conditions and characteristics surrounding each event.</p> <p><strong>Attributes <br></strong>1. SITE Geographic location of the human-shark interactions (New Caledonia or Reunion Island).<br>2. N: Sequential number of the incident.<br>3. DATE: Date of the human-shark interactions (YYYY-MM-DD format).<br>4. YEAR: Year of the incident.<br>5. MONTH: Month of the incident.<br>6. DAY: Day of the week when the incident occurred.<br>7. HOUR: Time of the incident (24-hour format).<br>8. HOURTYPO: Time range category (e.g., 13-15 for 1 PM to 3 PM).<br>9. SEASON: Season during which the human-shark interactions occurred (Summer, Winter, etc.).<br>10. WEEKEND: Indicates whether the incident occurred on a weekend (Weekend or Week).<br>11. MORNING: Time of day (Morning, Afternoon, etc.).<br>12. ZONE: Specific zone or region within the site classified into five categories: East, West, Loyalty Islands, Noumea or Greater Noumea (Noumea town, Mont-Dore, Paita and Dumbea), Bays of Noumea <br>13. WIND: Windward or leeward side.<br>14. RAINJ: Rainfall on the day of the incident (in millimeters).<br>15. RAINJ3: Cumulative rainfall over the past three days (in millimeters).<br>16. SWELL: Swell height (in meters).<br>17. CLOUD: Cloud cover percentage.<br>18. TURB: Water turbidity (e.g., Slightly turbid).<br>19. SCOREMOON: Lunar phase during the incident (e.g., Full or new moon).<br>20. GENDER: Gender of the victim (Male or Female).<br>21. AGE: Age of the victim.<br>22. ACTIVITY: Activity the victim was engaged in during the attack (e.g., Spearfisher, Swimmer).<br>23. GRAV: Severity of the injury (e.g., Significant bite, Minor bite).<br>24. INJURY: Outcome of the attack (e.g., Non-fatal).<br>25. SHARKTYPE: Type of shark involved (if identified).<br>26. SHARKCAT: Category of the shark (e.g., Great White, Tiger Shark).<br>27. SHARKHEIGHT: Estimated length of the shark (in meters).</p> <p><strong>Usage Notes</strong><br>This dataset is intended for researchers and analysts studying human-shark interactions patterns, environmental influences on shark behavior, and risk factors associated with human-shark interactions. It provides comprehensive details necessary for performing statistical analyses and comparative studies between New Caledonia and Reunion Island.</p> <p><strong>Data Sources</strong><br>The data has been compiled from various local and international databases, publications, and eyewitness accounts to ensure accuracy and completeness.<br><br></p> <div> <p><strong>1. Bibliography</strong><br>- Taglioni, F., Guiltat, S. & Delsaut, M. <em>Datasets of Human–shark interactions in New Caledonia (1980-2022)</em>. <a href="https://doi.org/10.5281/zenodo.12549370" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12549370</a> (Data from 1980 to 2022)<br>- P. Tirard, <em>Requins du caillou</em>, Edition Philippe Tirard, Nouméa, 2011 (Data from 1980 to 2010)<br>- F. Dreyer, <em>Les attaques de requin en Nouvelle- Calédonie,</em> MD Doctorate, Université de Strasbourg, Strasbourg, 2001, p. 208 (Data from 1980 to 2000)<br>- <em>Global Shark Attack file</em> (GSAF) (Data from 1980 to 2022)<br>- Various articles in the French national and Caledonian press (Data from 1980 to 2022)</p> <p><strong>2. Environmental factors<br></strong>- Rainfall: Météo France (French National Meteorological Service). Data extracted from the nearest rainfall station (32 stations) on the day of the human–shark interaction and cumulated rainfall over the previous three days<br>- Cloud cover expressed as percentages:* NOAA from 2008 to 2022;<br> * Weather forecasts from 1999 to 2022 (data from Météo France New Caledonia) ; from 1980 to 1998: Dreyer andTirard<br>- Swell height:<br> * WaveWatch 3 (NOAA) data, from 2008 to 2022<br> * Weather forecasts from 1999 to 2022 (data from Météo France New Caledonia)<br> * From 1980 to 1998: Dreyer and Tirard <br>- Turbidity: empirical estimated score from the swell, rainfall, and knowledge of the benthic substrate, Dreyer and Tirard, information from the local press; score: authors<br>- Moon phase: ephemeris; score: authors</p> <p><strong>3. Contextual factors<br></strong>- Date, human–shark interaction location, time, shark species and height according to:<br> * Dreyer, 2001<br> * Tirard, 2011<br> * Global Shark Attack File (GSAF)<br> * Various articles in the New Caledonia local press and the French national (analyses from 1980 to 2022)</p> <p><strong>4. Victim characteristics<br></strong>- Age of victim, sex (male/female), victim’s activity at time of human–shark interaction, severity of injuries (score: authors), according to:<br> * Dreyer, 2001<br> * Tirard, 2011<br> * Global Shark Attack File (GSAF)<br> * Various articles in the New Caledonia local press and the French national (analyses from 1980 to 2022)</p> <p> </p> <p> </p> <br> <p><br><br></p> <p> </p> </div>
Interactive study of human PTCD2 protein
<p>We investigated the intractome of human PTCD2 protein, a putative key player in non-canonical mitochondrial RNA processing, by Proximity-dependent Biotin Identification (BioID) followed by ESI-LC-MSMS. The dataset includes the results of the Mass spec analysis with detailed reports made by the proteomics core facility, University of Geneva. </p>
FIG. 7 in The biometry of prehistoric Alpine sheep: exploring four millennia of human-sheep interaction by means of osteometry
FIG. 7. — LSI of sheep bone width measurements in Early Bronze Age Barche di Solferino.
FIG. 8 in The biometry of prehistoric Alpine sheep: exploring four millennia of human-sheep interaction by means of osteometry
FIG. 8. — LSI of sheep bone width measurements in Early Iron Age Terra Negra.
Reducing risky interactions: Identifying barriers to the successful management of human-wildlife conflict in an urban parkland
<p>Managing activities that result in human-wildlife conflict is a challenging goal for modern scientists and managers. In recent years, the self-motivated feeding of wildlife by humans has garnered popularity but with consequent risks for the health and safety of both parties. This has resulted in calls for management in areas of high contact, e.g., parklands. Traditional controls are typically utilised (i.e., signage, patrols), yet their success is varied, leading to a rise in research aiming to improve them. This research has primarily focused on language and design, with little attention paid to the role that audience type (i.e., international tourists versus locals/residents) may play in their success. Proportions in audience type present can vary both between parks and spatially within a single park, however, controls are usually applied homogeneously with no consideration for how response may vary between these groups.</p> <p>Here, we performed a robust before-after study across two summers using a wild fallow deer population in a public park that are commonly fed by visitors as our model. We deployed controls, following best practices as outlined by the literature, and tested their overall effectiveness. We then identified key areas with differences in visitor type proportions and tested for variation in success between them.</p> <p>We found that the number of visitors feeding the deer significantly decreased overall after the introduction of controls, although interactions were not eliminated entirely. We discovered that the effectiveness of these controls varied with changes in visitor type, with the most positive effects occurring in areas with more international tourists and no significant effect occurring in areas dominated by resident visitors. Notably, of the food offerings remaining, the proportion of foods that could be perceived as 'nutritionally beneficial' increased in both sites, marking overall changes in the behaviours of even those visitors who refused to stop feeding.</p> <p>These findings highlight the importance of target audience research in human-wildlife conflict management. We recommend that authorities aiming to reduce these interactions perform systematic surveys to identify the audience type present and cater controls accordingly to maximise their success.</p>
High temperatures and human pressures interact to influence mortality in an African carnivore
<p>1. The impacts of high ambient temperatures on mortality in humans and domestic animals are well understood. However much less is known about how hot weather affects mortality in wild animals. High ambient temperatures have been associated with African wild dog Lycaon pictus pup mortality, suggesting that high temperatures might also be linked to high adult mortality.</p> <p>2. We analysed mortality patterns in African wild dogs radio-collared in Kenya (0°N), Botswana (20°S) and Zimbabwe (20°S), to examine whether ambient temperature was associated with adult mortality.</p> <p>3. We found that high ambient temperatures were associated with increased adult wild dog mortality at the Kenya site, and there was some evidence for temperature associations with mortality at the Botswana and Zimbabwe sites.</p> <p>4. At the Kenya study site, which had the highest human impact, high ambient temperatures were associated with increased risks of wild dogs being killed by people, and by domestic dog diseases. In contrast, temperature was not associated with the risk of snare-related mortality at the Zimbabwe site, which had the second-highest human impact. Causes of death varied markedly between sites.</p> <p>5. Pack size was positively associated with survival at all three sites.</p> <p>6. These findings suggest that while climate change may not lead to new causes of mortality, rising temperatures may exacerbate existing anthropogenic threats to this endangered species, with implications for conservation. This evidence suggests that temperature-related mortality, including interactions between temperature and other anthropogenic threats, should be investigated in a greater number of species to understand and mitigate likely impacts of climate change.</p>
Artificial selection in human-wildlife feeding interactions
<p>The artificial selection of traits in wildlife populations through hunting and fishing has been well documented. However, despite their rising popularity, the role that artificial selection may play in non-extractive wildlife activities, e.g., recreational feeding activities, remains unknown.</p> <p>If only a subset of a population takes advantage of human-wildlife feeding interactions, and if this results in different fitness advantages for these individuals, then artificial selection may be at work. We have tested this hypothesis using a wild fallow deer population living at the edge of a capital city as our model population.</p> <p>In contrast to previous assumptions on the randomness of human-wildlife feeding interactions, we found that a limited non-random portion of an entire population is continuously engaging with people. We found that the willingness to beg for food from humans exists on a continuum of inter-individual repeatable behaviour; which ranges from risk-taking individuals repeatedly seeking and obtaining food, to shyer individuals avoiding human contact and not receiving food at all, despite all individuals having received equal exposure to human presence from birth and coexisting in the same herds together. Bolder individuals obtain significantly more food directly from humans, resulting in early interception of food offerings and preventing other individuals from obtaining supplemental feeding.</p> <p>Those females that beg consistently also produce significantly heavier fawns (300-500g heavier), which may provide their offspring with a survival advantage. This indicates that these interactions result in disparity in diet and nutrition across the population, impacting associated physiology and reproduction, and may result in artificial selection of the begging behavioural trait.</p> <p>This is the first time that this consistent variation in behaviour and its potential link to artificial selection has been identified in a wildlife population and reveals new potential effects of human-wildlife feeding interactions in other species across both terrestrial and aquatic habitats. 22-Jun-2022 --</p>
Raw output data from ColabFold modelling for the paper 'Interaction of C21ORF2 with a domain of NEK1 mutated in human diseases is vital for NEK1 function in human cells'
<p><strong>Raw output data from ColabFold modelling for the paper 'Interaction of C21ORF2 with a domain of NEK1 mutated in human diseases is vital for NEK1 function in human cells'</strong></p> <p><strong>File descriptions:</strong></p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_1_model_1_fixed.pdb</strong><br> ColabFold output PDB file - Rank 1 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_2_model_2_fixed.pdb</strong><br> ColabFold output PDB file - Rank 2 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_3_model_4_fixed.pdb</strong><br> ColabFold output PDB file - Rank 3 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_4_model_3_fixed.pdb</strong><br> ColabFold output PDB file - Rank 4 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_5_model_5_fixed.pdb</strong><br> ColabFold output PDB file - Rank 5 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_coverage.png</strong><br> ColabFold output chart - MSA sequence coverage</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_PAE.png</strong><br> ColabFold output chart - PAE for each model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_plddt.png</strong><br> ColabFold output chart - predicted IDDT per position</p> <p><strong>Supplementary Excel file 1</strong><br> List of residues predicted to be involved in intermolecular interactions, and the type of interaction (based on PDB files for each models, generated using BIOVIA Discovery Studio 2021)</p>
Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES) Dataset - Anonymized
<p>Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES) and this corresponding dataset aim to provide tools for measuring user enjoyment from an external perspective to supplement self-reported user enjoyment responses in human-robot interaction research, with future potential application for autonomous detection of user enjoyment in real-time in robots and agents for adapting conversations contingently to provide enjoyable and long-lasting interactions.</p> <p>The dataset consists of 25 older adults' (12 men, 13 women) open-domain dialogue with an autonomous companion robot with an integrated large language model (GPT-3.5, text-davinci-003) from participatory design workshops conducted in March 2023. The conversations are annotated for user enjoyment based on HRI CUES by 3 expert annotators, as described in the paper (arXiv:2405.01354). Robot architecture and participatory design workshops are described in DOI: 10.21203/rs.3.rs-2884789/v1.</p> <p><strong>Exchanges</strong> file contains the participant ID, the number of the turn (conversation exchange by Robot-Participant response), the start and end of the turn, the anonymized transcript for the turn, and three annotator scores for the user enjoyment in the exchange. </p> <p><strong>Overall </strong>file contains the participant ID, self-reported user perception scores from the questionnaire ("I was satisfied with my conversation with the robot", "It was fun talking to the robot", "The conversation with the robot was interesting", "It felt strange talking to the robot") and three annotator scores for the user enjoyment in the overall interaction.</p> <p>The conversations are in Swedish. Participants' mean age is 74.6 (SD=5.8). 20 participants had no prior interaction with a robot, and only one had previously talked with a robot. The average interaction duration is 7.4 min (SD=1.5) with 12 to 29 turns. Each turn lasts 5 to 61 seconds (M=17.7, SD=7.2). The total duration of the interactions is 174 min, corresponding to 590 turns. </p> <p><em>Videos of the interactions are available upon request, contingent upon a signed agreement to maintain data confidentiality in accordance with GDPR regulations.</em></p> <p>Anonymization macros:</p> <p>[P_NAME]: Participant's name (may include surname). The robot always uses the first name even when the surname is given.</p> <p>[NAME_REMOVED]: A name of another person mentioned by the participant.</p> <p>[LOCATION_REMOVED]: Small town/village/area where the participant lives or lived.</p> <p>[MEDICAL_INFO_REMOVED]: Medical information shared by the participant.</p> <p>[AGE_REMOVED]: Participant's or other person's age.</p> <p>[INFORMATION_REMOVED]: Sensitive information shared by the participant.</p> <p>[MISTAKEN_NAME]: Speech recognition error resulted in the name being misunderstood.</p>
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.