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Data for "Measurement of the atom-surface van der Waals interaction by transmission spectroscopy in a wedged nano-cell"
<p>The data presented in publication <a href="http://arxiv.org/abs/1905.02783">"Measurement of the atom-surface van der Waals interaction by transmission spectroscopy in a wedged nano-cell"</a> . Published version: <a href="https://doi.org/10.1103/PhysRevA.100.022503">https://doi.org/10.1103/PhysRevA.100.022503</a></p> <p>The data are in HDF5 format, with associated metadata.</p> <p>To see examples of how to use the data, and the theoretical model for analysis, see <a href="https://github.com/thermal-vapours/TAS-Transmission-Atom-Surface">https://github.com/thermal-vapours/TAS-Transmission-Atom-Surface </a></p>
THÖR-MAGNI: A Large-scale Indoor Motion Capture Recording of Human Movement and Interaction
<h1>The THÖR-MAGNI Dataset Tutorials</h1> <p>THÖR-MAGNI datasets is a novel dataset of accurate human and robot navigation and interaction in diverse indoor contexts, building on the previous <a href="https://ieeexplore.ieee.org/abstract/document/8954833/">THÖR dataset protocol</a>. We provide position and head orientation motion capture data, 3D LiDAR scans and gaze tracking. In total, THÖR-MAGNI captures <strong>3.5 hours of motion of 40 participants on 5 recording days</strong>.</p> <p>This data collection is designed around systematic variation of factors in the environment to allow building cue-conditioned models of human motion and verifying hypotheses on factor impact. To that end, THÖR-MAGNI encompasses 5 scenarios, in which some of them have different conditions (i.e., we vary some factor):</p> <ul> <li>Scenario 1 (plus conditions A and B): <ul> <li> Participants move in groups and individually;</li> <li> Robot as static obstacle;</li> <li> Environment with 3 obstacles and lane marking on the floor for <strong>condition B</strong>;</li> </ul> </li> </ul> <ul> <li> Scenario 2: <ul> <li> Participants move in groups, individually and transport objects with variable difficulty (i.e. bucket, boxes and a poster stand);</li> <li> Robot as static obstacle;</li> <li> Environment with 3 obstacles;</li> </ul> </li> </ul> <ul> <li>Scenario 3 (plus conditions A and B): <ul> <li> Participants move in groups, individually and transporting objects with variable difficulty (i.e. bucket, boxes and a poster stand). We denote each role as: <em>Visitors-Alone, Visitors-Group 2, Visitors-Group 3, Carrier-Bucket, Carrier-Box, Carrier-Large Object;</em></li> <li> Teleoperated robot as moving agent: in <strong>condition A</strong>, the robot moves with differential drive; in <strong>condition </strong>B, the robot moves with omni-directional drive;</li> <li> Environment with 2 obstacles;</li> </ul> </li> </ul> <ul> <li>Scenario 4 (plus conditions A and B): <ul> <li> All participants, denoted as <em>Visitors-Alone HRI</em> interacted with the teleoperated mobile robot;</li> <li> Robot interacted in two ways: in <strong>condition A</strong> (Verbal-Only), the Anthropomorphic Robot Mock Driver (ARMoD), a small humanoid NAO robot on top of the mobile platform, only used speech to communicate the next goal point to the participant; in <strong>condition B</strong> the ARMoD used speech, gestures and robotic gaze to convey the same message;</li> <li> Free space environment</li> </ul> </li> </ul> <ul> <li>Scenario 5: <ul> <li> Participants move alone (<em>Visitors-Alone</em>) and one of the participants, denoted as <em>Visitors-Alone HRI</em>, transport objects and interact with the robot;</li> <li> The ARMoD is remotely controlled by an experimenter and proactively offers help;</li> <li> Free space environment;</li> </ul> </li> </ul> <h2>Preliminary steps</h2> <p>Before proceeding, make sure to download the data from ZENODO</p> <h3>1. Directory Structure</h3> <p>├── CLiFF_Maps <- Directory for CLiFF Maps for all files</p> <p> ├── Files <- Directory for the csv files</p> <p> ├── Readme.md</p> <p>├── CSVs_Scenarios <- Directory for aligned data for all scenarios</p> <p> ├── Scenario_1 <- Directory for the csv files for Scenario 1</p> <p> ├── Scenario_2 <- Directory for the csv files for Scenario 2</p> <p> ├── Scenario_3 <- Directory for the csv files for Scenario 3</p> <p> ├── Scenario_4 <- Directory for the csv files for Scenario 4</p> <p> ├── Scenario_5 <- Directory for the csv files for Scenario 5</p> <p>├── docs</p> <p> ├── tutorials.md <- Tutorials document on how to use the data</p> <p>├── Lidar_sample</p> <p> ├── Files <- Directory for sample files</p> <p> ├── 170522_SC3B_1 <- Directory for the pcd files</p> <p> ├── 170522_SC3B_1.csv <- Synchronization file with QTM</p> <p> ├── manual_view_point.json <- json file with manual view point for visualization</p> <p> ├── requirements.txt <- script pip requirements</p> <p> ├── visualize_pcd.py <- script visualize the lidar data</p> <p> ├── Readme.md</p> <p>├── maps <- Directory for maps of the environment (PNG files) and offsets (json file)</p> <p> ├── offsets.json <- Offsets of the map with respect to the global coordinate frame origin</p> <p> ├── {date}_SC{sc_id}_map.png <- Maps for `date` in {1205, 1305, 1705, 1805} and `sc_id` in {1A, 1B, 2, 3}</p> <p> ├── 3009_map.png <- Map for the Scenarios 4A, 4B and 5</p> <p>├── MP4_Videos</p> <p> ├── Files <- Directory for the mp4 files</p> <p> ├── pupil_scene_camera_instrinsics.json <- json file with the intrinsics of pupil camera</p> <p>├── TSVs_RAWET <- Directory for the TSV files for the Raw Eyetracking data for all Scenarios</p> <p> ├── synch_info.csv <- Event markers necessary to align motion capture with eyetracking data</p> <p> ├── Files <- Directory with all the raw eyetracking TSV files</p> <p>├── goals_positions.csv <- File with the goals locations</p> <p> </p> <h3>2. Data Structure and Dataset Files</h3> <p>Withing each Scenario directory, each csv file contains:</p> <p><strong>2.1. Headers</strong></p> <p>The dataset metadata overview contains important information found in the CSV file headers. This reference is designed to help users understand and use the dataset effectively. The headers include details such as FILE_ID, which provides information on the date, scenario, condition, and run associated with each recording. The header of the document includes important quantities such as the number of frames recorded (N_FRAMES_QTM), the count of rigid bodies (N_BODIES), and the total number of markers (N_MARKERS).</p> <p>It also provides information about the order of the contiguous rotation matrix (CONTIGUOUS_ROTATION_MATRIX), modalities measured with units, and specified measurement units. The text presents details on the eyetracking devices used in each recording, including their infrared sensor and scene camera frequencies, as well as an indication of the presence of eyetracking data.</p> <p>The header provides specific information about rigid bodies, including their names (BODY_NAMES), role labels (BODY_ROLES), and the number of markers associated with each rigid body (BODY_NR_MARKERS). Finally, the table lists all marker names used in the file.</p> <p>This metadata provides researchers and practitioners with essential guidance on recording information, data quantities, and specifics about rigid bodies and markers. It is a valuable resource for understanding and effectively using the dataset in the CSV files.</p> <p><strong>2.2. Trajectory Data</strong></p> <p>The remaining portion of the CSV file integrates merged data from the motion capture system and eye tracking devices, organized based on participants' helmet rigid bodies. Columns within the dataset include XYZ coordinates of all markers, spatial centroid coordinates, 6DOF orientation of the object's local coordinate frame, and <em>if available</em> eye tracking data, encompassing 2D/3D gaze coordinates, scene recording frame numbers, eye movement types, and IMU data.</p> <p>Missing data is denoted by "N/A" or an empty cell. Temporal indexing is facilitated by the "Time" or "Frame" column, indicating timestamps or frame numbers. The motion capture system records at 100Hz, Tobii Glasses at 50Hz (Raw); 25 Hz (Camera), and Pupil Glasses at 100Hz (Raw); 30 Hz (Camera). The dataset is structured around motion capture recordings, and for each rigid body, such as "Helmet_1," details per frame include XYZ coordinates of markers, centroid coordinates, and a 9-element rotational matrix describing helmet orientation.</p> <table> <tbody> <tr> <td><strong>Header</strong></td> <td><strong>Explanation</strong></td> </tr> <tr> <td>Helmet_1 - 1 X</td> <td>X-Coordinate of Marker Number 1</td> </tr> <tr> <td>Helmet_1 - 1 Y</td> <td>Y-Coordinate of Marker Number 1</td> </tr> <tr> <td>Helmet_1 - 1 Z</td> <td>Z-Coordinate of Marker Number 1</td> </tr> <tr> <td>Helmet_1 - [...]</td> <td><em>Same for Marker 2 and 3 of Helmet_1</em></td> </tr> <tr> <td>Helmet_1 Centroid_X</td> <td>X-Coordinate of the Centroid</td> </tr> <tr> <td>Helmet_1 Centroid_Y</td> <td>Y-Coordinate of the Centroid</td> </tr> <tr> <td>Helmet_1 Centroid_Z</td> <td>Z-Coordinate of the Centroid</td> </tr> <tr> <td>Helmet_1 R0</td> <td>1st Element of the CONTIGUOUS_ROTATION_MATRIX</td> </tr> <tr> <td>Helmet_1 R[..]</td> <td>Same for R1- R7</td> </tr> <tr> <td>Helmet_1 R8</td> <td>9th Element of the CONTIGUOUS_ROTATION_MATRIX</td> </tr> </tbody> </table> <p> </p> <p><strong>2.3. Eyetracking Data</strong></p> <p>The eye tracking data in the dataset includes 16 participants, providing a comprehensive dataset of over 500 minutes of recorded data across the different activities and scenarios with three different eyetracking devices. Devices are denoted with a special "Tracker_ID" in the dataset, i.e.:</p> <table> <tbody> <tr> <td><strong>Tracker ID</strong></td> <td><strong>Eyetracking Device</strong></td> </tr> <tr> <td>TB2</td> <td>Tobii 2 Glasses</td> </tr> <tr> <td>TB3</td> <td>Tobii 3 Glasses</td> </tr> <tr> <td>PPL</td> <td>Pupil Insivisible Glasses</td> </tr> </tbody> </table> <p>Gaze points are classified into fixations and saccades using the Tobii I-VT Attention filter, which is specifically optimized for dynamic scenarios with a velocity threshold of 100°. Eyetracking devices were systematically repeated after each 4-minute recording to account for natural variations in participants' eye shapes and to improve the gaze estimation algorithms. In addition, gaze estimation adjustments for the pupil invisible glasses were made after each 4-minute recording to mitigate potential drifts. It's worth noting that the scene cameras of the eye tracking glasses had different fields of view. The scene camera of the Pupil Invisible Glasses had a 1088x1080 image with both horizontal (HFOV) and vertical (VFOV) opening angles of 80°, while the Tobii Glasses provided a 1920x1080 image with different opening angles for Tobii Glasses 3 (HFOV: 95°, VFOV: 63°) and Tobii Glasses 2 (HFOV: 82°, VFOV: 52°).</p> <p><strong>NOTE AS OF 2024:</strong> <strong>Videos are NOW part</strong> of the dataset</p> <p>For one participant, wearing the Tobii Glasses 3 and Helmet_6, the data would be denoted as:</p> <table> <tbody> <tr> <td><strong>Header</strong></td> <td><strong>Explanation</strong></td> </tr> <tr> <td><em>Helmet_6 - [...]</em></td> <td><em>*X,Y,Z Coordinates for 5 markers*</em></td> </tr> <tr> <td><em>Helmet_6 [...]</em></td> <td><em>X,Y,Z Coordinates for 1 Centroid* </em></td> </tr> <tr> <td><em>Helmet_6 R[...]</em></td> <td><em>9 Elements of the CONTIGUOUS_ROTATION_MATRIX</em></td> </tr> <tr> <td> <p>Helmet_6 TB3_Accelerometer_[...]</p> </td> <td>Accelerometer data along the X,Y,Z Axis</td> </tr> <tr> <td>Helmet_6 TB3_Gyroscope_[...]</td> <td>Gyroscope data along the X,Y,Z Axis</td> </tr> <tr> <td>Helmet_6 TB3_Magnetometer_[...]</td> <td>Magnetometer data along the X,Y,Z Axis</td> </tr> <tr> <td>Helmet_6 TB3_G2D_[...]</td> <td>2D Eye tracking data (X,Y)</td> </tr> <tr> <td>Helmet_6 TB3_G3D_[...]</td> <td>3D Cyclopic Eye gaze Vector (X,Y,Z)</td> </tr> <tr> <td>Helmet_6 TB3_Movement</td> <td>Eye movement type (N/A, Fixation or Saccade)</td> </tr> <tr> <td>Helmet_6 TB3_SceneFNr</td> <td>Frame number of the scene camera recording </td> </tr> </tbody> </table> <h2>How to use and tools</h2> <p><a href="https://github.com/tmralmeida/magni-dash/tree/dash-public">magni-dash</a></p> <p><a href="https://magni-dash.streamlit.app">This</a> is a dashboard to quickly visualize our data: trajectories, speeds, eye-tracking data and LiDAR visualization (for Scenario 3). If you cannot use the dashboard from the streamlit cloud service, just run it locally by following the <a href="https://github.com/tmralmeida/magni-dash/tree/dash-public">README File</a>.</p> <p><a href="https://github.com/tmralmeida/thor-magni-tools">thor-magni-tools</a></p> <p>To install and use the package, follow the instructions on the <a href="https://github.com/tmralmeida/thor-magni-tools/blob/main/README.md">README file</a> . This package comprises:</p> <ul> <li>3D trajectory restoration: agents in the scene wore an helmet. The helmet is equipped with markers, which are tracked by the Mocap system. 3D trajectory restoration stands for <a href="https://github.com/tmralmeida/thor-magni-tools/blob/main/thor_magni_tools/preprocessing/cfg.yaml#L3">two different ways</a> of aggregating the trackings of the various markers in each helmet: (1) <em>3D-restoration</em> and (2) <em>3D-best marker</em>. The former applies an average over the locations of all visible markers while the latter uses the marker with highest tracking duration.</li> <li>3D pre-processing of restored trajectories: interpolation, downsampling and smoothing. To run the 3D pre-processing, check <a href="https://github.com/tmralmeida/thor-magni-tools?tab=readme-ov-file#preprocessing#preprocessing">this</a>.</li> <li>trajectory analysis: trajectory-related metrics like tracking duration (in seconds), number of 8s <em>tracklets</em>, motion speed, path efficiency score, and minimal distance between people. To run the trajectory analysis, check <a href="https://github.com/tmralmeida/thor-magni-tools?tab=readme-ov-file#preprocessing#analysis">this</a>.</li> </ul>
Meter-Scale Magma-Water Interaction Experiments
<p>These are video and other sensor data of experiments in which "magma" — that is: volcanic rock, re-melted at ca. 1300°C — interacts with liquid water. The experiments aim to better understand the escalation behavior of the processes involved when magma comes into contact with liquid water.</p> <p>The dataset will grow over time as data of new experiments is added.</p> <p><strong>Changes</strong></p> <ul> <li>Version 1.0: Add the <code>pr06</code> experiment.</li> <li>Version 0.11: Add the <code>pr05</code> experiment.</li> <li>Version 0.10: Add the <code>ir16</code> experiment.</li> <li>Version 0.9: Add the <code>ir15</code> experiment.</li> <li>Version 0.8: Add the <code>ir14</code> experiment.</li> <li>Version 0.7: Add the <code>ir13</code> experiment.</li> <li>Version 0.6: Add the <code>ir12</code> experiment.</li> <li>Version 0.5: Add the <code>ir07</code> experiment.</li> <li>Version 0.4: Add the <code>ir06</code> experiment.</li> <li>Version 0.3: Add the <code>ir05</code> experiment.</li> <li>Version 0.2: Add the <code>ir04</code> experiment.</li> <li>Version 0.1: Start with experiment <code>ir03</code>.</li> </ul>
Interactions between bats and agricultural insect pests worlwide
<p>This database illustrates the interactions between bats and agricultural insect pests detected conducting a systematic review in October 2022, entitled "<strong>Pest suppression by bats and management strategies to favour it: a global review</strong>", to be published in the journal Biological Reviews.</p> <p>Methodology applied:</p> <p>We compiled a comprehensive list of agricultural insect pests occurring in temperate and tropical regions. Since no more recent public documents or published lists were available, we extracted the main agricultural insect pests cited in Hill (1983, 1987). Note that species might be considered pests in certain regions while not in others, meaning that this comprehensive list will need careful review by entomologists and local or regional experts for use in agricultural management.</p> <p>We assembled a first list of 1,237 insect pest species or genera extracted from Hill (1987, 1983). We then conducted a literature search in the ISI Web of Science using the R package wosr. We searched for any indexed document containing the following terms in the topic field: "pest species name" AND "bat*", where ‘pest species name’ refers to each of the 1237 species. After the first check of the articles found, we added 562 new pest species to the first list, which were not included in Hill (1987, 1983), but were mentioned in the papers found. Thus, the updated list consisting of 1799 insect pest species was used again to perform the same literature search with the R package wosr. In addition, we also performed three literature searches including the following terms: (i) "bat" or "bats", "diet*", and "insect*"; (ii) "bat" or "bats", "predat*", and "insect*"; (iii) "bat" or "bats", "diet*", and "arthropod*". We identified a total of 1125 articles, of which we retained only those that identified bat prey at the genus or species level (N = 95).</p> <p>Predator - prey interactions were extracted from the articles reviewed and added in this data set, showing each bat species with the insect pest species it consumed, as well as the method used to confirm predation.</p>
Trabecular bone – screw interaction. Micro-CT models and experimental push-in results.
<p>The dataset disclosed herein was employed to build the screw-bone interaction models, specifically for tasks related to screw push-in simulation.</p>
California's Central Valley Project Improvement Act Predation Contact Point Study - 2022: Predator-prey interactions under low artificial lighting in a laboratory setting
The highest rates of piscivorous predation in the field have been recorded during crepuscular light levels associated with sunrise and sunset or artificial lighting at night (ALAN). We conducted a laboratory study where groups of predator-naïve, hatchery-raised juvenile rainbow trout (Oncorhynchus mykiss) were exposed to natural-origin piscivorous largemouth bass (Micropterus salmoides) under three light treatments representative of brighter crepuscular periods or direct ALAN illumination (“high” treatment), dimmer crepuscular periods or sky glow from ALAN (“medium” treatment), and night or no ALAN (“low” treatment). We then statistically evaluated potential associations between light treatment, prey group cohesion, and predator activity.
warmXtrophic: plant community responses to the individual and interactive effects of climate warming and herbivory across multiple years at Kellogg Biological Station Long-Term Ecological Research Sites (KBS LTER), Michigan, USA, and University of Michigan Biological Station (UMBS), Michigan, USA.
Climate change has both direct and indirect effects on ecological communities. Whereas most climate change ecology experiments manipulate abiotic drivers to measure direct effects of climate on species or communities, fewer quantify the indirect effects through biotic interactions, especially over multiple sites and years. In this factorial experiment we manipulate temperature through open-top chambers, and the level of insect herbivory through insecticide. At two early successional field sites separated by 3 degrees of latitude and 3°C of mean annual temperature (University of Michigan Biological Station, Pellston, MI and Kellogg Biological Station, Hickory Corners, MI), 6 replicate 1-m2 plots per treatment were installed in May 2015. 12 plots per site are at ambient temperature, 12 are warmed with year-round non-UV filtering polycarbonate and wood frame construction OTCs for tall-stature plants (Welshofer et al. 2018 MEE). Insecticide reduces insect herbivory in half the plots (Welshofer et al. 2018 Oecologia). Over the course of the experiment, OTCs warmed the plant communities by 1.9°C-3.0°C on average over the growing season. Each year, through 2021, plant traits and community responses were measured at the species level: plant phenology (green-up, flowering, flowering duration, seed set); plant percent cover (aerial % cover of the 1m2 plot); plant traits (specific leaf area, C and N content), herbivory damage to leaves, and plant species biomass (only in 2021). Further methodological details are found within each response variable metadata. This experiment is ongoing and further data package updates are planned. L0 data is available upon request. R scripts can be found here: https://github.com/SpaCE-Lab-MSU/warmXtrophic. The biotic and abiotic community context and relative strengths of direct vs. indirect effects may yield ecological surprises under climate change unless addressed together. Large-scale experiments like this one can improve our ability to unde
warmXtrophic plant-soil interaction greenhouse experiment, Kellogg Biological Station, Hickory Corners, MI, 2021
Climate warming influences plant communities through both direct effects, such as changes in temperature, and indirect pathways mediated by changes in soil microbial communities. These microbe-mediated indirect effects may alter plant traits and ecosystem dynamics in ways that are often overlooked in studies focused solely on the direct impacts of warming. To test these microbe-mediated indirect effects, we used field-conditioned soil from a 7-year (2015-2021) warming experiment (warmXtrophic) in an early successional plant community in Hickory Corners, Michigan, USA at Michigan State University's Kellogg Biological Station Long-Term Ecological Research site. In a greenhouse during 2021, we assessed how warmed versus ambient soil inocula influenced plant growth and traits of two species: Trifolium pratense (red clover) and Phleum pratense (Timothy grass). We measured above, below, and total biomass, height, number of leaves, timing of germination, leaf % carbon and nitrogen, C:N ratio, specific leaf area (SLA), and greenness (a proxy for chlorophyll content). We also measured the timing of emergence of the cotyledon and first leaf for Trifolium pratense.
Hippocampal-neocortical interactions sharpen over time for predictive actions
Open the record for dataset details and reuse information.
CARESSES Interaction Logs
<p>The IL dataset has been produced by the CARESSES project (caressesrobot.org).</p> <p>The dataset is the collection of messages shared among the CARESSES components during interactions between the culturally competent robot and a person. Each IL file captures the events occurred during the encounter, the actions and status of the person (as perceived by the robot) and the actions of the robot, and it is acquired to allow offline analyses and replays of the events occurred during the interaction. Specifically, the IL dataset is organized as a collection of 3 automatically generated files per user:</p> <ul> <li><em>cahrimLog</em> is the log of the "Culture-Aware Human-Robot Interaction Module" of the CARESSES architecture, and it reports: (i) all messages received and sent by this module from/to the other components of the architecture, (ii) the list of actions performed by the robot during the interaction together with (ii-a) their actual parameters, (ii-b) starting and (ii-c) ending time of each action and (ii-d) errors raised during its execution, if any;</li> <li><em>speechLog</em> is the log of all dialogue instances between the robot and the person (with the person's utterances reported as understood by the robot);</li> <li><em>uAAL_log</em> is the log of all messages exchanges by the components of the CARESSES architecture (CKB, where cultural knowledge is stored; CSPEM, responsible for planning the actions of the robot; CAHRIM, discussed above).</li> </ul> <p>IL files are encoded in CSV format, which is among the most readable formats for information storage. Each line corresponds to a record, i.e. all the info related to a message shared by any of the software components of the culturally competent robot during an encounter with a person. A record is divided into fields, separated by a delimiter (i.e., a semicolon in our case).</p> <p>The IL dataset, as a collection of quantitative data describing interactions between a person and an assistive robot can be useful to researchers aiming at defining guidelines, best practices and standards in the field of Human-Robot Interaction. Specific points of interest include: 1) identifying which robot actions are most frequently requested by people, and how, to design and develop more useful and easier to use assistive robots; 2) identifying recurring sequences of request/action/response that a robot or system for Ambient Assisted Living could rely on to exhibit predictive behaviours; 3) identifying preferred topics for conversation (and related phrasings), to design and develop more personal and adaptive assistive devices.</p> <p>Please notice also that, according to the original Data Management Plan, Interaction Logs are collected in two separate stages of the project: first in a laboratory setting in the course of Task 5.6 (m23 – m27) for benchmarking purposes, and then in the course of real-world experiments with care home residents in Task 6.3 (m28-m33) and Task 6.4 (m28-m33).</p> <p>However, we ultimately decided to publish only the Interaction Logs produced in the course of Task 5.6, performed in the last months of the project with international students enrolled in the Masters course on Robotics Engineering at University of Genova. The deviation from the original plan is necessary to ensure a proper quality of the Interaction Logs, that are now linked to video clips providing ground truth for comparison, recorded and published after seeking consent of the participant according to the provisions of the GDPR, and available upon request sending an email to info@caressesrobot.org</p>
Drug-drug interactions of irinotecan, 5-fluorouracil, folinic acid and oxaliplatin for improved colorectal carcinoma treatment
<p>Research records and experimental data of the study "Drug-drug interactions of irinotecan, 5-fluorouracil, folinic acid and oxaliplatin for improved colorectal carcinoma treatment"</p>
INTERACT-II (INTERcomparison of Aerosol and Cloud Tracking - II)
<p>Following the previous efforts of INTERACT (INTERcomparison of Aerosol and Cloud Tracking), the INTERACT-II campaign used multi-wavelength Raman lidar measurements to assess the performance of an automatic compact micro-pulse lidar (MiniMPL) and two ceilometers (CL51 and CS135) in providing reliable information about optical and geometric atmospheric aerosol properties. The campaign took place at the CNR-IMAA Atmospheric Observatory (760 ma.s.l.; 40.60<sup>∘</sup> N, 15.72<sup>∘</sup> E) in the framework of ACTRIS-2 (Aerosol Clouds Trace gases Research InfraStructure) H2020 project. Co-located simultaneous measurements involving a MiniMPL, two ceilometers and two EARLINET multi-wavelength Raman lidars were performed from July to December 2016.</p> <p>All the data from the CIAO lidars, the MiniMPL and from theCHM15k, CS135 and the CT25K ceilometers, operating collocated and simultaneously during the INTERACT-II campaign, are provided here. Additional files for the correction of the MiniMPL incomplere overlap are also provided.</p> <p>The results of the campaign are described in detail in Madonna et al., 2018 (<a href="https://amt.copernicus.org/articles/11/2459/2018/">https://amt.copernicus.org/articles/11/2459/201</a>8/).</p>
CETAF-DiSSCo/COVID19-TAF biodiversity-related knowledge hub working group: indexed biotic interactions and review summary
<p>This data publication originated as part of developing a biodiversity-related knowledge hub on COVID-19 via COVID19-TAF - Communities Taking Action (https://cetaf.org/covid19-taf-communities-taking-action), a community-rooted initiative raised jointly by the Consortium of European Taxonomic Facilitaties (CETAF, https://cetaf.org) and Distributed Systems of Scientific Collections (DiSSCo, https://www.dissco.eu/).</p> <p>This archive contains the biodiversity datasets of interest identified in period 14 April-6 October 2020 through COVID19-TAF activities and subsequently indexed by Global Biotic Interactions (GloBI, https://globalbioticinteractions.org). GloBI provides open access to finding species interaction data (e.g., predator-prey, pollinator-plant, virus-host, parasite-host) by combining existing open datasets using open source software.</p> <p>These identified datasets (see references and reviews below) add to a growing collection of open species interaction datasets already indexed by GloBI. So, this data publication only includes a small subset of indexed datasets and include only datasets that were added as a direct consequence of COVID19-TAF activities of the biodiversity-related knowledge hub working group.</p> <p>If you have questions or comments about this publication, please open an issue at https://github.com/ParasiteTracker/tpt-reporting or contact the authors by email.</p> <p>Funding:<br> The creation of this archive was made possible in part by reporting software developed as part of the National Science Foundation award "Collaborative Research: Digitization TCN: Digitizing collections to trace parasite-host associations and predict the spread of vector-borne disease," Award numbers DBI:1901932 and DBI:1901926 . Also, this material is based upon work supported by the National Science Foundation under Grant No. DGE-1545433 .</p> <p>References:<br> Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005.</p> <p>GloBI Data Review Report</p> <p>Datasets under review:<br> - Geiselman, Cullen K. & Sarah Younger. 2020. Bat Eco-Interactions Database. www.batbase.org accessed via https://github.com/globalbioticinteractions/batbase/archive/9c65cfeee1a054f9db8cd8bf6892017fd1b3c840.zip on 2020-10-04T22:53:45.576Z<br> - Geiselman, Cullen K. and Tuli I. Defex. 2015. Bat Eco-Interactions Database. www.batplant.org accessed via https://github.com/globalbioticinteractions/batplant/archive/a2e1b57052244d5251d17e96ea61f58bea88975e.zip on 2020-10-04T22:54:28.727Z<br> - Daniel Becker, Gregory F Albery, Anna R Sjodin, Timothee Poisot, Tad Dallas, Evan A. Eskew, Maxwell J. Farrell, Sarah Guth, Barbara A Han, Nancy B Simmons, Colin J Carlson. 2020. Predicting wildlife hosts of betacoronaviruses for SARS-CoV-2 sampling prioritization. bioRxiv 2020.05.22.111344; doi: https://doi.org/10.1101/2020.05.22.111344 accessed via https://github.com/globalbioticinteractions/becker2020/archive/47c6ad28e1c5058f3c13ca69a59fdf21229e8d7f.zip on 2020-10-04T22:54:46.723Z<br> - Chen L, Liu B, Yang J, Jin Q, 2014. DBatVir: the database of bat-associated viruses. Database (Oxford). 2014:bau021. doi:10.1093/database/bau021 accessed via https://github.com/globalbioticinteractions/dbatvir/archive/a906d76e362484d3ca1edbe9683f672838ab70b0.zip on 2020-10-04T22:56:13.913Z<br> - Chen L, Liu B, Wu Z, Jin Q, Yang J, 2017. DRodVir: A resource for exploring the virome diversity in rodents. J Genet Genomics. 44(5):259-264. accessed via https://github.com/globalbioticinteractions/drodvir/archive/0346c0e8d4d66c6400e9965bd6a6aeed24cd7586.zip on 2020-10-04T23:06:04.368Z<br> - Agosti, Donat. 2020. Transcription of Linné, C. von, 1758. Systema naturae per regna tria naturae secundum classes, ordines, genera, species, cum characteribus, differentiis, synonymis, locis. Available at: http://dx.doi.org/10.5962/bhl.title.542 . accessed via https://github.com/globalbioticinteractions/linnaeus1758/archive/a818060080fa04a88dac6df1ae5b897304ae8877.zip on 2020-10-05T00:46:04.852Z<br> - Mollentze, Nardus, & Streicker, Daniel G. (2019). Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3516613 accessed via https://github.com/globalbioticinteractions/mollentze2019/archive/ad12dc74d03c3d992618f16c37cafb7f7ffd9d01.zip on 2020-10-05T00:50:55.878Z<br> - Eneida L. Hatcher, Sergey A. Zhdanov, Yiming Bao, Olga Blinkova, Eric P. Nawrocki, Yuri Ostapchuck, Alejandro A. Schäffer, J. Rodney Brister, Virus Variation Resource – improved response to emergent viral outbreaks, Nucleic Acids Research, Volume 45, Issue D1, January 2017, Pages D482–D490, https://doi.org/10.1093/nar/gkw1065 . accessed via https://github.com/globalbioticinteractions/ncbi-virus/archive/531a8d743d7adcf1153a19087e5d3c5b76750e3e.zip on 2020-10-05T00:53:53.646Z<br> - Olival, K. J., Hosseini, P. R., Zambrana-Torrelio, C., Ross, N., Bogich, T. L., & Daszak, P. (2017). Host and viral traits predict zoonotic spillover from mammals. Nature, 546(7660), 646–650. doi:10.1038/nature22975 accessed via https://github.com/globalbioticinteractions/olival2017/archive/f61070a5339d0e6c6e76d7eb4e2102decb52317d.zip on 2020-10-05T00:56:43.356Z<br> - Pensoft Darwin Core Archives with associateTaxa columns accessed via https://github.com/globalbioticinteractions/pensoft-dwca/archive/ee8831a2a391203f4fa8c05a0ddd927202b234bf.zip on 2020-10-05T00:56:51.868Z<br> - Pensoft Darwin Core Archives available via Integrated Publication Toolkit accessed via https://github.com/globalbioticinteractions/pensoft-ipt/archive/4ad4b47978324681289e36f8c2b247b1bcc97b1a.zip on 2020-10-05T00:58:01.912Z<br> - De Rojas M, Doña J, Dimov I (2020) A comprehensive survey of Rhinonyssid mites (Mesostigmata: Rhinonyssidae) in Northwest Russia: New mite-host associations and prevalence data. Biodiversity Data Journal 8: e49535. https://doi.org/10.3897/BDJ.8.e49535 accessed via https://github.com/globalbioticinteractions/pensoft-table/archive/3488e0397ca4e083d5eca6949951e426a75713e3.zip on 2020-10-05T00:58:03.647Z<br> - Marcus Guidoti, Tatiana Ruschel, Donat Agosti. 2020. Corona virus related biotic associations manually extracted from literature. Plazi. accessed via https://github.com/globalbioticinteractions/plazi-covid19/archive/326578b0d9f974760dcd2e962d86636a6487a6c0.zip on 2020-10-05T00:58:08.025Z<br> - Shaw, LP, Wang, AD, Dylus, D, et al. The phylogenetic range of bacterial and viral pathogens of vertebrates. Mol Ecol. 2020; 29: 3361– 3379. https://doi.org/10.1111/mec.15463 accessed via https://github.com/globalbioticinteractions/shaw2020/archive/bb9ab857b7fdbb4e931752d01b43d37b3ada77cf.zip on 2020-10-05T01:05:23.554Z<br> - OpenBiodiv. 2020. Annotated biotic interaction tables from Pensoft publications. accessed via https://github.com/pensoft/pensoft-interaction-tables/archive/bb7d1dc9f2eba220a61502e06e6114053fd30788.zip on 2020-10-05T03:03:23.372Z<br> - Quentin J. Groom. 2020. Bat interation data manually extracted from literature. accessed via https://github.com/qgroom/batinterations/archive/70108945f9014aa0ac1db920191867f7e151c793.zip on 2020-10-05T03:04:11.533Z</p> <p>Generated on:<br> 2020-10-06</p> <p>by:<br> GloBI's Elton 0.10.2<br> (see https://github.com/globalbioticinteractions/elton).</p> <p> </p> <p>Note that all files ending with .tsv are files formatted<br> as UTF8 encoded tab-separated values files.</p> <p>https://www.iana.org/assignments/media-types/text/tab-separated-values</p> <p><br> Included in this review archive are:</p> <p>README:<br> This file.</p> <p>review_summary.tsv:<br> Summary across all reviewed collections of total number of distinct review comments.</p> <p>review_summary_by_collection.tsv:<br> Summary by reviewed collection of total number of distinct review comments.</p> <p>indexed_interactions_by_collection.tsv:<br> Summary of number of indexed interaction records by institutionCode and collectionCode.</p> <p>review_comments.tsv.gz:<br> All review comments by collection.</p> <p>indexed_interactions_full.tsv.gz:<br> All indexed interactions for all reviewed collections.</p> <p>indexed_interactions_simple.tsv.gz:<br> All indexed interactions for all reviewed collections selecting only sourceInstitutionCode, sourceCollectionCode, sourceCatalogNumber, sourceTaxonName, interactionTypeName and targetTaxonName.</p> <p>datasets_under_review.tsv:<br> Details on the datasets under review.</p> <p>elton.jar:<br> Program used to update datasets and generate the review reports and associated indexed interactions.</p> <p><br> datasets.zip:<br> source datasets collected by elton in process of executing the generate_report.sh script.</p> <p>generate_report.sh:<br> program used to generate the report</p> <p>generate_report.log:<br> log file generated as part of running the generate_report.sh script</p>
Using the Tea Bag Index to unravel how interactions between an antibiotic (Trimethoprim) and endocrine disruptor (17a-estradiol) affect aquatic microbial activity: Supporting Dataset 1
<p>The constant release of complex mixture of pharmaceuticals, including antimicrobials and endocrine disruptors, into the aquatic environment. These have the potential to affect aquatic microbial metabolism and alter biogeochemical cycling of carbon and nutrients. We used the Tea Bag Index (TBI) for decomposition within a series of contaminant exposure experiments to test how interactions between an antibiotic (trimethoprim) and endocrine disruptor (17a-estradiol) affects microbial activity in an aquatic system. The TBI is a citizen science tool used to test microbial activity by measuring the differential degradation of green and rooibos tea as proxies for labile and recalcitrant organic matter decomposition. Here we present the raw data on pharmaceutical exposures and the mass loss of the Rooibos and Green tea bags within the experiment. From Tea Bag mass loss we then calculated the Stabilisation Factor (S) and Initial Decomposition Rate of the labile organic matter fraction.</p>
Bioactivity deep learning for structure-free compound-protein interaction
<p>CPI2M data for "<strong>Bioactivity deep learning for structure-free compound-protein interaction</strong>".</p> <p>CPI2M_main_Ki.csv: Bioactivity data with <strong>pKi </strong>activity type. Used for model training and internal validation.</p> <p>CPI2M_main_Kd.csv: Bioactivity data with <strong>pKd</strong> activity type. Used for model training and internal validation.</p> <p>CPI2M_main_EC50.csv: Bioactivity data with <strong>pEC50 </strong>activity type. Used for model training and internal validation.</p> <p>CPI2M_main_IC50.csv: Bioactivity data with <strong>pIC50 </strong>activity type. Used for model training and internal validation.</p> <p>CPI2M_few_Ki.csv: Bioactivity data with <strong>pKi </strong>activity type. Used for external validation.</p> <p>CPI2M_few_Kd.csv: Bioactivity data with <strong>pKd </strong>activity type. Used for external validation.</p> <p>CPI2M_few_EC50.csv: Bioactivity data with <strong>pEC50 </strong>activity type. Used for external validation.</p> <p>CPI2M_few_IC50.csv: Bioactivity data with <strong>pIC50 </strong>activity type. Used for external validation.</p> <p>potency.csv: BIoactivity data with <strong>pPotency </strong>activity type. Not used currently but can be potentially adopted as classification data for customized use.</p> <p>percentage.csv: BIoactivity data with <strong>Percentage Inhibition </strong>activity type. Not used currently but can be potentially adopted as classification data for customized use.</p> <p>Protein_pretrained_feat.zip: pre-calculated protein feature files with UniProt ID naming. <strong>Should be unzipped</strong> before start model training with CPI2M data.</p> <p> </p> <p>For each .csv data, columns include "<strong>smiles</strong>" (ligand SMILES), "<strong>exp_mean</strong>" (nM bioactivity), "<strong>y</strong>" (neg.log nM, final label), "<strong>cliff_mol</strong>" (whether activity cliff or not), "<strong>split</strong>" (splitting label by activity cliff), "<strong>Uniprot_id</strong>" (UniProt ID for protein), "<strong>Sequence</strong>" (wildtype sequence for protein), and "type_id" (bioactivity type token, pKi =0, pKd=1, pEC50=2, pIC50=3).</p> <p> </p> <p>Please find the project code at https://github.com/gu-yaowen/GGAP-CPI</p> <p> </p>
The Pathogen-Host Interactions Database, version 4.18
<p>PHI-base is an online database (available at <a href="http://www.phi-base.org">phi-base.org</a>) that catalogues experimentally verified pathogenicity, virulence and effector genes from fungal, oomycete and bacterial pathogens, which infect animal, plant, fungal and insect hosts. PHI-base is a valuable resource in the discovery of genes in medically and agronomically important pathogens, which may be potential targets for chemical intervention.</p> <p>Each entry in PHI-base is curated by domain experts and is supported by strong experimental evidence (for example, gene disruption and gene complementation experiments), as well as literature references in which the original experiments are described. Each gene in PHI-base is presented with its nucleotide sequence and deduced amino acid sequence (available in a FASTA file), as well as a detailed description of the predicted protein's function during the host infection process. To facilitate data interoperability, we have annotated genes using ontologies, controlled vocabularies, and links to external sources (including UniProt, Gene Ontology, Enzyme Commission, NCBI Taxonomy, EMBL, PubMed and FRAC).</p> <p>This PHI-base dataset is a Frictionless Data Package that contains an export of the PHI-base database in CSV format (comma-separated values), plus a FASTA file with sequences for each gene in the database. This version of the dataset, version 4.18, contains 5,828 publications, covering 23,497 pathogen–host interactions and 10,614 pathogen genes across 335 pathogen species and 265 host species.</p>
Interactive maps for the visualization of ESRIUM automated driving tests with various EGNSS localization solutions
<p>In order to make the test results available to a broader audience in an easy manner, we have generated interactive maps. These maps are attached to this report and can be viewed in a web-browser. </p><p>Due to the large number of datasets, we have color-coded them on the map and in the menu. An arbitrary number of datasets can be selected at a time.</p><p>Due to the high accuracy of the EGNSS receivers, one can clearly identify the lane on which the vehicle was driving, and where the vehicle was performing a lane-change. However, the satellite/areal-images are not perfectly geo-referenced, thus one can notice a slight offset between satellite/areal-images and real-world lanes.</p><p> </p><p><strong>How to use the map?</strong></p><ul><li>The map can be used in a similar manner than other map-applications, such as google maps. By using the mouse, you can set the focus on the area of your interest. By using the +/- buttons (top left), you can zoom in/out.</li><li>By hovering over the layer-symbol (top right), a popup emerges. Here, you can select different background-tiles (such as satellite/areal-images). In addition, you can select different datasets which should be visualized on the map.</li></ul><p><strong>Background-tiles:</strong></p><ul><li>Basemap – Sat - Satellite/Areal images (from Basemap) -Symbolic map with high resolution (from Basemap)</li><li>Basemap – HighDPI Symbolic map with high resolution (from Basemap)</li><li>OpenStreetMap - Symbolic map (from OpenStreetMap)</li><li>OpenTopoMap - Symbolic map including topology information (from OpenTopoMap)</li></ul><p><strong>Datasets:</strong></p><ul><li>GNSS (Vehicle) - Position of vehicle, according to on-board GPS receiver</li><li>EGNSS (AsteRx SB3 Pro+) - Position of vehicle, according to AsteRx SB3 Pro+ receiver</li><li>EGNSS (mosaic-X5) - Position of vehicle, according to mosaic-X5 receiver</li><li>EGNSS (mosaic-H) - Position of vehicle, according to mosaic-H receiver</li><li>PVT Mode: EGNSS (AsteRx SB3 Pro+) - PVT Mode of AsteRx SB3 Pro+ receiver</li><li>PVT Mode: EGNSS (mosaic-X5) - PVT Mode of mosaic-X5 receiver</li><li>PVT Mode: EGNSS (mosaic-H) - PVT Mode of mosaic-H receiver</li><li>in-lane Offset Change-Request - Position, at which an in-lane offset change (relative to middle of the current lane) was requested via C-ITS</li><li>Lane Change to left - Position, at which a lane-change towards left was performed </li><li>Lane Change to right - Position, at which a lane-change towards right was performed</li></ul><p>Interactive maps are attached are two precision levels one with 4 and the other in 7 digits. The list files and the corresponding test conditions are listed below. </p><p>Test velocities [km/h]: 90, 110, 130 </p><p>interactive map files: </p><p>speed: 90 km/h</p><ul><li>Testrun_01.html</li><li>Testrun_03.html</li><li>Testrun_04.html</li></ul><p>speed: 110 km/h</p><ul><li>Testrun_05.html</li><li>Testrun_06.html</li><li>Testrun_07.html</li></ul><p>speed: 130 km/h </p><ul><li>Testrun_08.html</li><li>Testrun_09.html</li><li>Testrun_10.html</li></ul>
Cherri - Accurate detection of functional RNA-RNA interactions sites
<p><strong>CheRRI</strong> - Pipeline for the Identification of putative RNA-RNA interaction sites.</p> <p> </p> <p>This repository contains all CheRRI's models computed and mentioned in the content.txt, listing data and their descriptions. All models can be used to classify interaction sites in CheRRI's eval mode.</p> <p> </p> <p>The source code for CheRRI is avalbile on <a href="https://github.com/BackofenLab/Cherri#install-cherri-conda-package">GitHub</a> and can be cited using this Software Heritage citation:</p> <ul> <li><span>Müller T, Mautner S, Videm P, Eggenhofer F, Raden M, Backofen R (2024) CheRRI - Accurate classification of the biological relevance of putative RNA-RNA interaction sites (Version 0.8). [Computer software]. Software Heritage, <a href="https://archive.softwareheritage.org/swh:1:snp:ebac091117f9c46fb5f0fedd3ef23ec2905ced6c;origin=https://github.com/BackofenLab/Cherri">https://archive.softwareheritage.org/swh:1:snp:ebac091117f9c46fb5f0fedd3ef23ec2905ced6c;origin=https://github.com/BackofenLab/Cherri</a></span></li> </ul> <div> <div> <div> <p>The pipeline contains Machine Learning segments which were annotated using DOME:</p> </div> </div> </div> <ul> <li><span><a href="https://dome.ds-wizard.org/projects/74d0e01c-6374-41e9-93b8-2889d6a8fe25">https://dome.ds-wizard.org/projects/74d0e01c-6374-41e9-93b8-2889d6a8fe25</a></span></li> </ul>
Supplementary material to: Long-term (bio)deterioration of Fe-containing and Fe-depleted sandstones: An experimental insight into biotic and abiotic interactions.
<p>This dataset includes: micorphotographs, scanning electron microscope images and related EDS spectra, thermal analysis (DSC-TG), grain size distribution. Abbreviations used in the supplementary file names refer to: GMB (growth medium inoculated with the bacteria, Pseudomonas fluorescens), GM (sterile growth medium), ARE (artificial root exudates), H2O (water), NR (Sample Nowa Ruda), Z (Sample Żerkowice ŻR).</p>
EDICTOR 3: Interactive Tool for Computer-Assisted Language Comparison
This software offers the most recent and mostly stable version of the EDICTOR tool, also available for direct usage from <a href="https://edictor.org">edictor.org/</a>.
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.