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400 results for “Trust”
Figure 8 in A contribution to the knowledge of the butterfly fauna of Maputo Special Reserve, Mozambique from African Natural History Research Trust expeditions (Papilionoidea)
Figure 8 – Vegetation map of Maputo Special Reserve adapted from De Boer (2000). Collecting sites are numbered as they appear in the Materials & Methods. 'Woodland' sensu De Boer was separated by the researchers into 'Closed' and 'Grassy' woodland.
Figure 3 in A contribution to the knowledge of the butterfly fauna of Maputo Special Reserve, Mozambique from African Natural History Research Trust expeditions (Papilionoidea)
Figure 3 – Afrogegenes letterstedti ♀ pre-vaginal plate, the diagnostic shallow indentation indicated by a black arrow (see De Jong & Coutsis, 2017 for a full explanation)
Figure 1 in A contribution to the knowledge of the butterfly fauna of Maputo Special Reserve, Mozambique from African Natural History Research Trust expeditions (Papilionoidea)
Figure 1 – Some of the major habitat types encountered in the MSR: sand thicket (A), sand forest (B), hygrophilous grassland (C), dune grassland-dune forest ecotone (D)
Extended Evaluation Data of TrADS: a Trust-Aware Decentralized Social Network
<p>This dataset includes the evaluation data for the Paper "TrADS: a Trust-Aware Decentralized Social Network".<br>Within the ZIP File the following files are included in the dataset:</p> <ul> <li><strong>survey.pdf:</strong> The survey as PDF print. IFrames of TrADS used during the survey are hidden in the PDF.</li> <li><strong>all.xlsx:</strong> An Excelfile containing all the following .CSV files as worksheets.</li> <li><strong>raw_data.csv:</strong> The raw data exported from the used survey tool of the conducted empircal user study.</li> <li><strong>group1_unfiltered.csv:</strong> All participants' data of group 1.</li> <li><strong>group2_unfiltered.csv:</strong> All participants' data of group 2.</li> <li><strong>group1.csv:</strong> All filtered participants' data of group 1, who correctly answered the control questions.</li> <li><strong>group1_ueq_data.csv:</strong> All filtered participants's ueq+ question responses of group 1. Only including the questions 1 - 4 but not the question about dimension importance (q5).</li> <li><strong>group1_ueq_importance.csv:</strong> All filtered participants's ueq+ question responses about personal importance of group 1. Only including the question 5 about dimension importance.</li> <li><strong>group1_ueq_kpis:</strong> Including the ueq+ KPI values of all participants in group 1.</li> <li><strong>group2.csv:</strong> All filtered participants' data of group 2, who correctly answered the control questions.</li> <li><strong>group2_ueq_data.csv:</strong> All filtered participants's ueq+ question responses of group 2. Only including the questions 1 - 4 but not the question about dimension importance (q5).</li> <li><strong>group2_ueq_importance.csv:</strong> All filtered participants's ueq+ question responses about personal importance of group 2. Only including the question 5 about dimension importance.</li> <li><strong>group2_ueq_kpis:</strong> Including the ueq+ KPI values of all participants in group 2.</li> <li><strong>participants.csv:</strong> General information about participants grouped by both groups and joint.</li> <li><strong>likert_questions.csv:</strong> Mean Values and Standard Deviations (Std) of all statements rated on a 5-point Likert scale. It includes Means and Stds for Group 1, Group 2, Group 1 + Group 2 concatinated, and the values of the first user study published in the previous paper on TrADS <a href="https://zenodo.org/records/10641724" target="_blank" rel="noopener">(also available in previous dataset on Zenodo)</a>.</li> </ul>
Tarmachan Automatic Weather Station (National Trust for Scotland/University of Dundee)
<p>Mountain weather station on the south-facing slopes of Meall nan Tarmachan on the National Trust for Scotland's National Nature Reserve (NNR). The site is at 710 m above sea level, overlooking Loch Tay. Snowcover has historically been an important part of the climate of the site, but in recent years has become increasingly transient.</p> <p>The NNR is the most important in Scotland for its arctic alpine flora, we are noticing changes in species distribution and to habitats indicative of the effects of climate change. Hence the establishment of this weather station to allow changes in climate at altitude to be monitored. This site installed May 2018 ~20 m beyond the headwall of a disused quarry. Winds may cause undercatch of rainfall (though the gauge is of an aerodynamic design) and more particularly snow. Recorded wind speeds may underestimate winds generally around the mountain owing to effects of the quarry wall.</p> <p>An earlier site was operated nearby in the early 2000s but sadly data have been lost.</p> <p>Data are recorded on a Campbell Scientific CR1000 data logger, running with 10 s scan rate and 15 min logging interval.</p> <p>Snow depths may be inferred by examining TCDT (temperature-corrected depth to target) data - obtained from a SR50A sensor on an arm c. 2.3 m above ground. These are available only for limited periods in 2019 and again winter 2021/22, and are now discontinued. However, snow cover can be inferred by examining the differential between air temperature and ground temperature: the ground sensor is insulated when snow covers the ground.</p> <p>Sensor details: see metdata</p> <p>Real-time data are displayed graphically at <a href="https://hydro-data.dundee.ac.uk/tarmachan" target="_blank" rel="noopener">https://hydro-data.dundee.ac.uk/tarmachan</a> (no downloads)</p>
Remote Immune Monitoring: Need, Opportunities and Challenges - Professor Kourosh Saeb-Parsy (University of Cambridge & Cambridge University Hospitals NHS Foundation Trust)
<p>This video is the seventh talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Remote Immune Monitoring: Need, Opportunities and Challenges - Professor Kourosh Saeb-Parsy (University of Cambridge & Cambridge University Hospitals NHS Foundation Trust)</p> <p>Bio: Professor Kelvin Tsoi is an Epidemiologist specialized in Digital Health. His research interests focus on digital innovation in chronic disease management, including mobile and telecare application for hypertension management, technological implementation and social engagement for cognitive screening, artificial intelligent application on electronic health records. He also works as the traditional epidemiologist on evidence-based medicine and population cohort studies. He obtained his Bachler Degree from Department of Statistics and Doctor of Philosophy from School of Public Health in the Chinese University of Hong Kong. He further received post-doctoral training in the Division of Gastroenterology and Hepatology, Department of Medicine and Therapeutics. He was also appointed as a Director of CUHK JC Bowel Cancer Education Centre to promote colorectal cancer screening. In 2011, he worked as a research scientist in Hospital Authority. He led projects covering a wide range of service areas on chronic diseases, such as service demand projection for schizophrenia and dementia. The experience of database management enhanced his understanding of the HA database structures. In 2013, he was invited to join the interdisciplinary team for Big Data research and worked closely with a team of engineers and data scientists. Currently, Professor Tsoi is an Associate Professor in JC School of Public Health and Primary Care, SH big Data Decision Analytics Research Centre and JC Institute of Ageing. I matriculated as a medical student at Fitzwilliam College in 1993. My interest in biomedical research was developed during my Part II year studying Anatomy A (neurosciences and developmental biology) and I subsequently enrolled on the MB-PhD programme. I completed my doctoral thesis in neurophysiology of circadian rhythms in 2000 and qualified as a medical doctor in 2001. While studying for my PhD, I pursued an active interest in teaching and started supervising undergraduates at Fitzwilliam (and other colleges) in 1998. I served as MCR President in 1999, became a Fellow in 2003 and Director of Studies in Clinical Medicine in 2004. I pursued a career in surgery after graduation and was appointed as a University Lecturer in Transplant Surgery in 2012.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/ANZKGxj87E0</p>
Data for research article "Privacy Explanations – A Means to End-User Trust"
<p>Research data for article "<strong>Privacy Explanations – A Means to End-User Trust</strong>". This package includes the survey and its results.</p>
Can we use hydraulic handbooks in blind trust? Two examples from a real-world complex hydraulic system
<p>This database includes the data used to produce the results for the following article:</p> <p>Bellos, V., Kossieris, P., Efstratiadis, A., Papakonstantis, I., Papanicolaou, P., Dimas, P., Makropoulos, C. 2022. Can we use hydraulic handbooks in blind trust? Two examples from a real-world complex hydraulic system. 7th IAHR Europe Congress, September 7th – 9th, 2022, Athens, Greece (accepted paper for oral presentation, in press).</p>
Movement data set for trust assessment (Drapebot robot cell/Profactor)
<p>In the Drapebot project, a worker collaborates with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from two standard Trust questionnaire (1. Trust perception scale - HRI, Schaefer 2016; 2. Trust in industrial human robo collaboration, Charalambous, et.al. 2016).</p> <p>For this data set, data has been collected for the draping task from 21 participants all familiar with working with large industrial manipulators. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements).</p> <p><strong>Data organization</strong></p> <p>There are 21 files for 21 participants. The name of the files is PID01, where the number 01 is the participant. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data:</p> <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> <p>See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a></p> <p>For more information on each specific data and/or sensors please see the xsens manual (Link above)</p> <p><strong>Data Annotation</strong></p> <p>In each .xlsx file the first tab (sheet) is called "Markers". It annotates the starting frame of the individual tasks. The annotations are pickup, draping, return and some files may contain a also a "fail" annotation. Failed attempts should not be taken into consideration for model training.</p> <p>The file trustscores.xlsx includes the results of the trust questionaires for each participant (scores for the individual items as well as the calculated overall trust scores).</p> <p>Items for Trust perception scale - HRI, Schaefer 2016:</p> <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> <p>Items for Trust in industrial human robo collaboration, Charalambous, et.al. 2016:</p> <ul> <li>The way the robot moved made me uncomfortable</li> <li>I felt I could rely on the robot to do what it was supposed to do </li> <li>The speed at which the gripper picked up and released the components made me uneasy </li> <li>I felt safe interacting with the robot</li> <li>I knew the gripper would not drop the components</li> <li>The size of the robot did not intimidate me</li> <li>The robot gripper did not look reliable</li> <li>I was comfortable the robot would not hurt me</li> <li>I trusted that the robot was safe to cooperate with</li> <li>The gripper seemed like it could be trusted</li> </ul> <p> </p> <p><span>K.</span><span> </span><span>E.</span><span> </span><span>Schaefer,</span><span> </span><span>Measuring</span><span> </span><span>Trust</span><span> </span><span>in</span><span> </span><span>Human</span><span> </span><span>Robot</span><span> </span><span>Interactions: </span><span>Development</span><span> </span><span>of</span><span> </span><span>the</span><span> </span><span>“Trust</span><span> </span><span>Perception</span><span> </span><span>Scale-HRI”</span><span>.</span><span> </span><span>Boston, </span><span>MA:</span><span> </span><span>Springer</span><span> </span><span>US,</span><span> </span><span>2016,</span><span> </span><span>pp.</span><span> </span><span>191–218.</span><span> </span></p> <p><span>G. Charalambous, S. Fletcher, and P. Webb, “The development of </span><span>a scale to evaluate trust in industrial human-robot collaboration,” </span><span>International Journal of Social Robotics</span><span>, vol. 8, pp. 193–209, 2016.</span></p>
Movement data set for trust assessment (Drapebot robot cell/Dallara)
<p>In the Drapebot project, a worker collaborates with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from two standard Trust questionnaire (1. Trust perception scale - HRI, Schaefer 2016; 2. Trust in industrial human robo collaboration, Charalambous, et.al. 2016).</p> <p>For this data set, data has been collected for the draping task in a near-production setting from 5 participants all familiar with carbon-fibre draping. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements).</p> <p><strong>Data organization</strong></p> <p>There are 5 files for 5 participants. The name of the files is PID01, where the number 01 is the participant. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data:</p> <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> <p>See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a></p> <p>For more information on each specific data and/or sensors please see the xsens manual (Link above)</p> <p><strong>Data Annotation</strong></p> <p>In each .xlsx file the first tab (sheet) is called "Markers". It annotates the starting frame of the individual tasks. The annotations are pickup, draping, return and some files may contain a also a "fail" annotation. Failed attempts should not be taken into consideration for model training.</p> <p>The file trustscores.xlsx includes the results of the trust questionaires for each participant (scores for the individual items as well as the calculated overall trust scores).</p> <p>Items for Trust perception scale - HRI, Schaefer 2016:</p> <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> <p>Items for Trust in industrial human robo collaboration, Charalambous, et.al. 2016:</p> <ul> <li>The way the robot moved made me uncomfortable</li> <li>I felt I could rely on the robot to do what it was supposed to do </li> <li>The speed at which the gripper picked up and released the components made me uneasy </li> <li>I felt safe interacting with the robot</li> <li>I knew the gripper would not drop the components</li> <li>The size of the robot did not intimidate me</li> <li>The robot gripper did not look reliable</li> <li>I was comfortable the robot would not hurt me</li> <li>I trusted that the robot was safe to cooperate with</li> <li>The gripper seemed like it could be trusted</li> </ul> <p> </p> <p>K. E. Schaefer, Measuring Trust in Human Robot Interactions: Development of the “Trust Perception Scale-HRI”. Boston, MA: Springer US, 2016, pp. 191–218.</p> <p>G. Charalambous, S. Fletcher, and P. Webb, “The development of a scale to evaluate trust in industrial human-robot collaboration,” International Journal of Social Robotics, vol. 8, pp. 193–209, 2016.</p>
Movement data set for trust assessment (Drapebot robot cell/DLR)
<p>In the Drapebot project, a worker collaborates with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from two standard Trust questionnaire (1. Trust perception scale - HRI, Schaefer 2016; 2. Trust in industrial human robo collaboration, Charalambous, et.al. 2016).</p> <p>For this data set, data has been collected for the draping task from 21 participants all familiar with working with large industrial manipulators. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements).</p> <p><strong>Data organization</strong></p> <p>There are 21 files for 21 participants. The name of the files is PID01, where the number 01 is the participant. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data:</p> <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> <p>See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a></p> <p>For more information on each specific data and/or sensors please see the xsens manual (Link above)</p> <p><strong>Data Annotation</strong></p> <p>In each .xlsx file the first tab (sheet) is called "Markers". It annotates the starting frame of the individual tasks. The annotations are pickup, draping, return and some files may contain a also a "fail" annotation. Failed attempts should not be taken into consideration for model training.</p> <p>The file trustscores.xlsx includes the results of the trust questionaires for each participant (scores for the individual items as well as the calculated overall trust scores).</p> <p>Items for Trust perception scale - HRI, Schaefer 2016:</p> <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> <p>Items for Trust in industrial human robo collaboration, Charalambous, et.al. 2016:</p> <ul> <li>The way the robot moved made me uncomfortable</li> <li>I felt I could rely on the robot to do what it was supposed to do </li> <li>The speed at which the gripper picked up and released the components made me uneasy </li> <li>I felt safe interacting with the robot</li> <li>I knew the gripper would not drop the components</li> <li>The size of the robot did not intimidate me</li> <li>The robot gripper did not look reliable</li> <li>I was comfortable the robot would not hurt me</li> <li>I trusted that the robot was safe to cooperate with</li> <li>The gripper seemed like it could be trusted</li> </ul> <p> </p> <p>K. E. Schaefer, Measuring Trust in Human Robot Interactions: Development of the “Trust Perception Scale-HRI”. Boston, MA: Springer US, 2016, pp. 191–218.</p> <p>G. Charalambous, S. Fletcher, and P. Webb, “The development of a scale to evaluate trust in industrial human-robot collaboration,” International Journal of Social Robotics, vol. 8, pp. 193–209, 2016.</p>
Dataset: First Trust NASDAQ Clean Edge Smart Grid Infrastructure Index Fund (GRID) 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.
Dataset: First Trust Small Cap Core AlphaDEX Fund (FYX) 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.
Dataset: First Trust Dorsey Wright Dynamic Focus 5 ETF (FVC) 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.
Dataset: First Trust Small Cap Value AlphaDEX Fund (FYT) 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.
Dataset: First Trust Dorsey Wright Focus 5 ETF (FV) 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.
Dataset: First Trust Nasdaq Transportation ETF (FTXR) 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.
Dataset: First Trust Nasdaq Bank ETF (FTXO) 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.
Dataset: First Trust Nasdaq Semiconductor ETF (FTXL) 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.
Dataset: First Trust Nasdaq Oil & Gas ETF (FTXN) 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.
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.