Find research datasets worth reusing

Mangrove Coast Collaborative Project, Post-hurricane Irma mangrove forest structure data, Rookery Bay NERR, February 2022 - March 2023

The dataset describes the structure, composition, and condition of mangrove forests in Rookery Bay National Estuarine Research Reserve (NERR) assessed Feb 2022 - Mar 2023, approximately 5 years post Hurricane Irma (2017) and concurrent with Hurricane Ian (Sep 2022). The dataset includes information on each stem greater than or equal to 1 cm DBH (diameter at breast height) rooted within 69 100 m2 circular plots. Information collected includes site ID, location, species, DBH, status (live or dead), damage associated with the hurricane, presence/absence of regrowth, presence/absence of adventitious roots, whether or not stem is part of a multi-stemmed individual, and the canopy conditions (whether the tree has a canopy or is only sprouting at the base of the tree or trunk). This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).

Mangrove Coast Collaborative Project, Post-hurricane Irma mangrove forest understory data, Rookery Bay NERR, February 2022 - March 2023

This dataset describes the structure and composition of the mangrove forest understory in Rookery Bay National Estuarine Research Reserve (NERR) assessed Feb 2022 - Mar 2023, approximately 5 years post Hurricane Irma (2017) and concurrent with Hurricane Ian (2022). The dataset includes information on the number of seedlings and saplings of each species as sampled in four 1 m2 quadrats in each 100 m2 structural sampling plot. The dataset also includes counts of the five tallest pneumatophores occurring in each quadrat. The height of pneumatophores was used as a proxy for the average maximum high water level in a plot. This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).

Mangrove Coast Collaborative Project, Post-hurricane Maria mangrove forest understory data, Jobos Bay NERR, March 2022 - August 2022

This dataset describes the structure and composition of the mangrove forest understory in Jobos Bay National Estuarine Research Reserve (NERR) assessed approximately 5 years after disturbance from Hurricane Maria (2017). The dataset includes information on the number of seedlings and saplings of each species as sampled in four 1 m2 quadrats in each 100 m2 structural sampling plot. The dataset also includes counts of the five tallest pneumatophores occurring in each quadrat. The height of pneumatophores was used as a proxy for the average maximum high water level in a plot. This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).

Mangrove Coast Collaborative Project, Post-hurricane Maria mangrove forest structure data, Jobos Bay NERR, March 2022 - August 2022

The dataset describes the structure, composition, and condition of mangrove forests in Jobos Bay National Estuarine Research Reserve (NERR) assessed approximately 5 years after disturbance from Hurricane Maria (2017). The dataset includes information on each stem greater than or equal to 1 cm DBH (diameter at breast height) rooted within 64 100 m2 circular plots. Information collected includes site ID, location, species, DBH, status (live or dead), damage associated with the hurricane, presence/absence of regrowth, presence/absence of adventitious roots, whether or not stem is part of a multi-stemmed individual, and the canopy conditions (whether the stem has a leafed canopy or is only sprouting at the base if live). This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).

Mangrove Coast Collaborative Project, Post-hurricane Maria mangrove forest coarse woody debris data, Jobos Bay NERR, March 2022 - August 2022

This dataset describes the quality and size of coarse woody debris (downed woody debris > 7.5 cm in diameter) for each mangrove forest plot in Jobos Bay National Estuarine Research Reserve (NERR) assessed approximately 5 years after disturbance from Hurricane Maria (2017). Data was collected along three 20 m transects beginning at each plot center point and heading toward a randomly-selected azimuth. This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).

Mangrove Coast Collaborative Project, Post-hurricane mangrove forest coarse woody debris data, Rookery Bay NERR, February 2022 - March 2023

This dataset describes the quality and size of coarse woody debris (downed woody debris > 7.5 cm in diameter) for each mangrove forest plot in Rookery Bay National Estuarine Research Reserve (NERR) assessed approximately 5 years after disturbance from Hurricane Irma (2017) and concurrent with Hurricane Ian (2022). Data was collected along three 20 m transects beginning at each plot center point and heading toward a randomly-selected azimuth. This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).

Mangrove Coast Collaborative Project, Post-hurricane mangrove forest downed woody debris data, Rookery Bay NERR, February 2022 - March 2023

This dataset describes the quantity and size distribution of downed woody debris for each mangrove forest plot in Rookery Bay National Estuarine Research Reserve (NERR) assessed approximately 5 years after disturbance from Hurricane Irma (2017) and concurrent with Hurricane Ian (2022). Data was collected along three 20 m transects beginning at each plot center point and heading toward a randomly-selected azimuth. This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).

Mangrove Coast Collaborative Project, Post-hurricane Maria mangrove forest downed woody debris data, Jobos Bay NERR, March 2022 - August 2022

This dataset describes the quantity and size distribution of downed woody debris for each mangrove forest plot in Jobos Bay National Estuarine Research Reserve (NERR) assessed approximately 5 years after disturbance from Hurricane Maria (2017). Data was collected along three 20 m transects beginning at each plot center point and heading toward a randomly-selected azimuth. This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).

opencc-by-4.0Oct 2016View details →

S3 | NORMANCT15 | NORMAN Collaborative Trial Targets and Suspects

This is the collection associated with list S3 NORMANCT15 on the NORMAN Suspect List Exchange. <a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a> S3 | NORMANCT15 | NORMAN Collaborative Trial Targets and Suspects Schymanski et al. 2015. DOI: <a href="http://link.springer.com/article/10.1007/s00216-015-8681-7">10.1007/s00216-015-8681-7</a> Upload 22/3/2020: added merged InChIKey file for PubChem data extraction. 27/6/2025: added merged CSV

opencc-by-4.0Nov 2024View details →

RoHuCAD: Robots and Humans Collaborative Anomaly Detection

<h1>RoHuCAD: Robots and Humans Collaborative Anomaly Detection</h1> RoHuCAD is a dataset of human-robot collaboration in a robotic workshop (check <code>workshop_layout.png</code>). Two robots (collaborative manipulator - cobot, autonomous mobile robot - AMR) assist three human operators in assembly of electronic devices. There are two 8-min long recordings in the dataset. They mostly follow the same scenario, with slightly different anomalies. The data is in ROS Noetic rosbag format. <h2>Included data </h2> <ul> <li>RGBD camera data (color + depth) <ul> <li>3 cameras: <a href="https://www.intelrealsense.com/depth-camera-d435i/">Intel Realsense D435i</a></li> <li>color and depth data at 6 frames per second</li> <li>Intrinsic calibration data</li> <li>Extrinsic calibration data (positions and orientations)</li> </ul> </li> <li>Information about positions of robots <ul> <li>AMR: <a href="https://www.ez-wheel.com/en/development-kit-for-agv-and-amr">Ez-Wheel SWD® Starter Kit</a></li> <li>Cobot: <a href="https://www.universal-robots.com/products/ur10-robot/">Universal Robots UR10e</a></li> </ul> </li> </ul> <h2>Annotations</h2> Annotations of specific anomalies are included (CSV file with columns: event_id, tstart, tend, event_type, person_id, camera_id) <ul> <li>Gestures / poses <ul> <li>BENT</li> <li>T-POSE (hands horizontally to the sides)</li> <li>L+R-UP (both hands up)</li> <li>RH-UP (right hand up)</li> <li>LH-UP (left hand up)</li> <li>SQUAT</li> <li>HI-POSE (waving)</li> </ul> </li> <li>Unsafe behaviour <ul> <li>Human in robot working area</li> <li>Standing back to (moving) robot</li> <li>Looking at phone</li> <li>Human in the way of AMR</li> </ul> </li> <li>Normal activities <ul> <li>Assembling/Working</li> <li>Loading/unloading AMR</li> </ul> </li> </ul> <h2>ROS topics</h2> <ul> <li><code>/tf </code></li> <li><code>/tf_static</code></li> <li><code>/joint_states</code></li> <li>cam_ws2_box <ul> <li><code>/cam_ws2_box/color/camera_info</code></li> <li><code>/cam_ws2_box/color/image_raw/compressed</code></li> <li><code>/cam_ws2_box/depth_registered/camera_info</code></li> <li><code>/cam_ws2_box/depth_registered/image_rect_raw</code></li> </ul> </li> <li>cam_ta2_ws2 <ul> <li><code>/cam_ta2_ws2/color/camera_info</code></li> <li><code>/cam_ta2_ws2/color/image_raw/compressed</code></li> <li><code>/cam_ta2_ws2/depth_registered/camera_info</code></li> <li><code>/cam_ta2_ws2/depth_registered/image_rect_raw</code></li> </ul> </li> <li>cam_ta1_ws2 <ul> <li><code>/cam_ta1_ws2/color/camera_info</code></li> <li><code>/cam_ta1_ws2/color/image_raw/compressed</code></li> <li><code>/cam_ta1_ws2/aligned_depth_to_color/camera_info</code></li> <li><code>/cam_ta1_ws2/aligned_depth_to_color/image_raw</code></li> </ul> </li> </ul> <h2>Acknowledgement</h2> The work leading to these results has received funding from the European Union’s Horizon Europe research and innovation programme within the ULTIMATE project under the Grant Agreement no 101070162.

opencc-by-4.0Nov 2019View details →

European Collaboration for Healthcare Optimisation (ECHO) Indicators Definition Crosswalks

European Collaboration for Healthcare Optimisation (ECHO) Indicators Definition Crosswalks ECHO indicators rationale and code definition mapped out in ICD-9 and ICD-10 (for diagnoses) and ICD-9, NOMESCO, OPCS-4, ACHI and Leustungkatalog.

opencc-by-4.0Oct 2021View details →

PE-HRI-temporal: A Multimodal Temporal Dataset in a robot mediated Collaborative Educational Setting

Please note that this dataset corresponds to the training data used in "Social robots as skilled ignorant peers for supporting learning "[7]. This (second) version of the dataset additionally includes labels (PE score and cluster labels for each datapoint).    This data set consists of multi-modal temporal team behaviors as well as learning outcomes collected in the context of a robot mediated collaborative and constructivist learning activity called JUSThink [1,2]. The data set can be useful for those looking to explore evolution of log actions, speech behavior, affective states, and gaze patterns for students to model constructs such as engagement, motivation, collaboration, etc. in educational settings.  In this data set, team level data is collected from 34 teams of two (68 children) where the children are aged between 9 and 12. There are two files:   PE-HRI_learning_and_performance.csv: This file consists of the team level performance and learning metrics which are defined below:  <ul> <li> last_error: This is the error of the last submitted solution. Note that if a team has found an optimal solution (error = 0) the game stops, therefore making last error = 0. This is a metric for performance in the task.  </li> <li> T_LG_absolute: It is a team-level learning outcome that we calculate by taking the average of the two individual absolute learning gains of the team members. The individual absolute gain is the difference between a participant’s post-test and pre-test score, divided by the maximum score that can be achieved (10), which grasps how much the participant learned of all the knowledge available. </li> <li> T_LG_relative: It is a team-level learning outcome that we calculate by taking the average of the two individual relative learning gains of the team members. The individual relative gain is the difference between a participant’s post-test and pre-test score, divided by the difference between the maximum score that can be achieved and the pre-test score. This grasps how much the participant learned of the knowledge that he/she didn’t possess before the activity.  </li> <li> T_LG_joint_abs: It is a team-level learning outcome defined as the difference between the number of questions that both of the team members answer correctly in the post-test and in the pre-test, which grasps the amount of knowledge acquired together by the team members during the activity </li> </ul> PE-HRI_behavioral_timeseries_w_labels.csv: In this file, for each team, the interaction of around 20-25 minutes is organized in windows of 10 seconds; hence, we have a total of 5048 windows of 10 seconds each. We report team level log actions, speech behavior, affective states, and gaze patterns for each window. More specifically, within each window, 26 features are generated in two ways:  <ol> <li>non-incremental</li> <li>incremental</li> </ol> A non-incremental type would mean the value of a feature in that particular time window while an incremental type would mean the value of a feature until that particular time window. The incremental type is indicated by an "_inc" at the end of the feature name. Hence, in the end, within each window, we have 52 values:  <ul> <li> T_add/(_inc): The number of times a team added an edge on the map in that window/(until that window). </li> <li> T_remove/(_inc): The number of times a team removed an edge from the map in that window/(until that window). </li> <li> T_ratio_add_rem/(_inc): The ratio of addition of edges over deletion of edges by a team in that window/(until that window). </li> <li> T_action/(_inc): The total number of actions taken by a team (add, delete, submit, presses on the screen) in that window/(until that window). </li> <li> T_hist/(_inc): The number of times a team opened the sub-window with history of their previous solutions in that window/(until that window). </li> <li> T_help/(_inc): The number of times a team opened the instructions manual in that window/(until that window). Please note that the robot initially gives all the instructions before the game-play while a video is played for demonstration of the functionality of the game.  </li> <li> T1_T1_rem/(_inc): The number of times either of the two members in the team followed the pattern consecutively: I add an edge, I then delete it in that window/(until that window). </li> <li> T1_T1_add/(_inc): The number of times either of the two members in the team followed the pattern consecutively: I delete an edge, I add it back in that window/(until that window). </li> <li> T1_T2_rem/(_inc): The number of times the members of the team followed the pattern consecutively: I add an edge, you then delete it in that window/(until that window). </li> <li> T1_T2_add/(_inc): The number of times the members of the team followed the pattern consecutively: I delete an edge, you add it back in that window/(until that window). </li> <li> redundant_exist/(_inc): The number of times the team had redundant edges in their map in that window/(until that window). </li> <li> positive_valence/(_inc): The average value of positive valence for the team in that window/(until that window). </li> <li> negative_valence/(_inc): The average value of negative valence for the team in that window/(until that window). </li> <li> difference_in_valence/(_inc): The difference of the average value of positive and negative valence for the team in that window/(until that window). </li> <li> arousal/(_inc): The average value of arousal for the team in that window/(until that window). </li> <li> gaze_at_partner/(_inc): The average of the the two team member's gaze when looking at their partner in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_at_robot/(_inc): The average of the the two team member's gaze when looking at the robot in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_other/(_inc): The average of the the two team member's gaze when looking in the direction opposite to the robot in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_at_screen_left/(_inc): The average of the the two team member's gaze when looking at the left side of the screen in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_at_screen_right/(_inc): The average of the the two team member's gaze when looking at the right side of the screen in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> T_speech_activity/(_inc): The average of the two team member's speech activity in that window/(until that window). Each individual member's speech activity is calculated as a percentage of time that they are speaking in that window/(until that window).  </li> <li> T_silence/(_inc): The average of the two team member's silence in that window/(until that window). Each individual member's silence is calculated as a percentage of time in that window/(until that window).  </li> <li> T_short_pauses/(_inc): The average of the two team member's short pauses over their speech activity in that window/(until that window). Each individual member's short pause refers to a brief pause of 0.15 seconds and is calculated as a percentage of time in that window/(until that window).  </li> <li> T_long_pauses/(_inc): The average of the two team members long pauses over their speech activity in that window/(until that window). Each individual member's long pause refers to a pause of 1.5 seconds and is calculated as a percentage of time in that window/(until that window).  </li> <li> T_overlap/(_inc): The average percentage of time the speech of the team members overlaps in that window/(until that window). </li> <li> T_overlap_to_speech_ratio/(_inc): The ratio of the speech overlap over the speech activity of the team in that window/(until that window). </li> </ul> Apart from these 52 values, within each window, we also indicate:  <ul> <li>team: The team to which the window belongs to.</li> <li>time_in_secs: Time in seconds until that window.</li> <li>window: The window number.</li> <li>normalized_time: The time when this window occurred with respect to the total duration of the task for a particular team. </li> <li>cluster_labels: The cluster number associated with each time window in reference to the productive and non-productive clusters found in [3]</li> <li>PE_score: The Productive Engagement score in each window</li> </ul> Lastly, we briefly elaborate on how the features are operationalised. We extract log behaviors from the recorded rosbags while the behaviors related to both gaze and affective states are computed through the open source library OpenFace [6] that returns both facial actions units (AUs) as well as gaze angles. For voice activity detection (VAD), that classifies if a piece of audio is voiced or unvoiced, we made use of the python wrapper for the open source Google WebRTC VAD. The literature that inspired our log, audio and video features as well as the tools used to extract them are described in more detail in [3,4]. However, in those papers, we make use of only the aggregate version of this data [5]. Please note that this dataset corresponds to the training data used in [7]. This (second) version of the dataset additionally includes labels (PE score and cluster labels for each datapoint).

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Mangrove Coast Collaborative Project, Hydrologic monitoring data in mangrove forests, Jobos Bay NERR, April 2024 - December 2024

The dataset describes the hydrologic conditions of the soil porewater (water level, conductivity, and temperature) at a depth of ~70 cm below ground in six mangrove forest locations in Jobos Bay National Estuarine Research Reserve (JBNERR) at 30-minute intervals between April 2024 to December 2024. Locations of minimal forest recovery following the effects of Hurricane Maria (September 2017) were identified and selected for hydrologic monitoring coincident with sites sampled for structural metrics in 2022. One reference site, defined as a site that was observed to be recovering following the hurricane, was selected in black mangrove forest. Two of the six sampling locations were selected to monitor effects of human encroachment on the western boundary of the reserve. These two sites were not coincident with structural sampling plots established in 2022. This dataset is associated with the MCC Catalyst Project entitled Limits of Resilience (2023-2025) funded by the National Estuarine Research Reserve System (NERRS) Science Collaborative.

edi52/100

Mangrove Coast Collaborative Project, Hydrologic monitoring data in mangrove forests, Rookery Bay NERR, April 2024 - December 2024

The dataset describes the hydrologic conditions of the soil porewater (water level, conductivity, and temperature) at a depth of ~70 cm below ground in six black mangrove forest locations in Rookery Bay National Esturarine Research Reserve (NERR) at 30-minute intervals between April 2024 to December 2024. Locations of minimal forest recovery following the effects of Hurricane Irma (September 2017) were identified and selected for hydrologic monitoring. The design consists of three sites in mainland/interior black mangroves and three sites on ocean-facing islands, all of which are located on the east side of Hurricane Irma eyewall. In each group, two of the sites selected were considered sites of minimal recovery whereas one site was selected as a reference (location of recovering mangroves). This dataset is associated with the MCC Catalyst Project entitled Limits of Resilience (2023-2025) funded by the National Estuarine Research Reserve System (NERRS) Science Collaborative.

opencc-by-4.0Aug 2019View details →

Measuring individual and group flow in collaborative improvisational dance.

Flow is a state of being fully absorbed and experiencing feelings of energised focus, deep involvement, and success in the process of doing things. Flow plays a vital role in innovation and creativity, as all such processes require high intrinsic motivation to break through to a new level of complexity of thoughts and ideas, while the social environment rarely provides sufficient extrinsic rewards to motivate people to extensive creative work. Meanwhile, the vast majority of creative activities have a primarily social character: e.g. theatre making, music, and dancing. Thus, group flow became central in group creativity research. Group flow shares many aspects with individual flow, but inevitably has differences, due to its collaborative nature. In this study, we compare individual and group flow in dance improvisation, to explore the cognitive processes and strategies underlying group improvisation and their relation to flow experience; in particular, those that might support the aspects of group flow that are dependent upon understanding the other group members’ states and intentions. To assess flow experience, we used a video-stimulated recall method, Flow (Łucznik, Loesche, 2017), which allowed participants to mark on the video-recording of the activity those moments when they remembered experiencing flow. We identified group flow as the moments when then the majority of a group declared themselves as being in flow. This dataset consists of the data and analysis used in the 'Measuring individual and group flow in collaborative improvisational dance.' article (in press).

opencc-by-4.0Feb 2019View details →

S35 | INDOORCT16 | Indoor Environment Substances from 2016 Collaborative Trial

This is the collection associated with list S35 INDOORCT16 on the NORMAN Suspect List Exchange. <a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a> S35  INDOORCT16  Indoor Environment Substances from 2016 Collaborative Trial Lists of GC-MS and LC-MS compounds and DSFP output, plus merged files from the Indoor Dust Collaborative Trial, 2016 provided by Peter Haglund (UMU) and Pawel Rostkowski (NILU). Details in Rostkowski et al. 2019 DOI: <a href="https://link.springer.com/article/10.1007/s00216-019-01615-6">10.1007/s00216-019-01615-6</a> Update 6 Feb 2020: two NA SMILES removed in CSV for PubChem upload. 17/7/2022: NA and N/A SMILES removed from CSV and XLSX, most replaced with structures; some are representative structures for classes.

opencc-by-4.0Aug 2021View details →

The relevance of signal timing in human-robot collaborative manipulation

Dataset version 1.0.1. The data collected here are attached to the following journal article: F. Cini*, T. Banfi*, G. Ciuti, L. Craighero, M. Controzzi, The relevance of signal timing in human-robot collaborative manipulation. Science Robotics Vol. 6 Issue 58, 2021. DOI: 10.1126/scirobotics.abg1308 To achieve a seamless human-robot collaboration, it is crucial that robots express their intentions without perturbating or interrupting the task that a human partner is performing at that moment. Although it has not received much attention so far, this issue is important when robots assist humans in physical and manipulation tasks. The main question addressed here is whether there is a more appropriate time to inform a human partner that a robot is requesting to pass them an object. This question is posed in a reference scenario where human individuals are involved in a continuous pick-and-place task that cannot be interrupted. Our findings showed that providing a cue at the beginning of a reach-to-grasp movement could severely interfere with the ongoing human action, increasing the number of errors made by humans, slowing down and degrading the smoothness of their arm movement, and deflecting their gaze. These disruptive interferences strongly decreased, until they disappeared, when the robot provided the cue to the human partners shortly after the participants picked up an object, identifying this as the best signaling timing. The results of this work showed how the signaling timing may have a decisive influence on the performances of the human-robot teamwork and contribute to understating the mechanisms underpinning the phenomenon of cognitive-motor interference in humans.