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300 results for “indoor”
Bluetooth indoor localization Dataset
<p><strong>Bluetooth indoor localization Dataset</strong>: Collected to perform experimentation on how bluetooth signal strengths can be used to determine one of the indoor locations. The dataset has 6 transmission power, from Tx01 to Tx06; and in each dataset have 5 features with the bluetooth RSSI because the enviroment have 5 BLE4.0 and 1 categorical target that is the sector where the person is located (15 sectors total).</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>
LoRaWAN Dense Indoor Sensor Network (DISN) Transmission Meta Data
<p>We present a large data of indoor Long Range Wide Area Network (LoRaWAN) network metadata to study Dense Indoor Sensor Networks (DISN). We collected 14 million transmissions from 390 sensors between date February 2020 and date September 2020. The transmissions have been received by 3 gateways across 8 floors and distances up to 64 m. The prototype will run in the background throughout the project and the data set will be regularly updated.</p> <p> </p>
A Danish high-resolution dataset for six office rooms with occupancy, indoor environment , heating, ventilation, lighting and room control monitoring
<p>A dataset containing measurement data for six office rooms in Aalborg Denmark.<br>All the measurements have been resampled to 5 minute resolution<br>The measurements consists of:</p> <ul> <li>BMS data for the rooms</li> <li>Occupancy for the rooms (from cameras)</li> <li>BMS data for the AHU supplying the rooms</li> <li>BMS data for the Heating system supplying the rooms</li> </ul> <p>Changes from v2<br>It was found that the pressure difference measurements across the exhaust fan was faulty and the following variables have therefore been removed:</p> <ul> <li>Ventilation:Fan__air_flow__exhaust</li> <li>Ventilation:Fan__pressure_difference__exhaust</li> </ul> <p>More data has been added, now increasing the dataset to span the rest of 2023. To better handle the changes between standard time and daylight-saving time the column named "timestamp" has been adjusted so the datetime format now follows the ISO 8601 format YYYY-MM-DDThh:mm:ss+hhmm. the +hhmm changes between 0100 (Danish standard time) and 0200 (Danish daylight-saving time).</p> <p> </p>
Inter-Chemical Correlation results for the study: HHEARx2017-1982 (Domestic Indoor PM and Childhood Asthma Morbidity (DISCOVER Study))
Title: Domestic Indoor PM and Childhood Asthma Morbidity (DISCOVER Study) <br>Species: Homo sapiens <br>Number of samples: 732 <br>Number of named analytes: 26 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=35 <br>
S35 | INDOORCT16 | Indoor Environment Substances from 2016 Collaborative Trial
<p>This is the collection associated with list S35 INDOORCT16 on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S35 INDOORCT16 <strong>Indoor Environment Substances from 2016 Collaborative Trial</strong></p> <p>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 <em>et al</em>. 2019 DOI: <a href="https://link.springer.com/article/10.1007/s00216-019-01615-6">10.1007/s00216-019-01615-6</a></p> <p>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.</p>
S101 | MTMDUST | List of chemicals characterised in indoor dust samples
<p>This is the collection associated with list S101 MTMDUST List of chemicals characterised in indoor dust samples on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>A list of 300 chemicals characterised with identification confidence level of ≥ 3 found in retrospective analysis of 30 dust samples from 4 different indoor settings (offices, households, preschools and various occupational settings) described in Dubocq et al. (2022) DOI: <a href="https://www.oaepublish.com/jeea/article/view/5192">10.20517/jeea.2022.23</a> which can help to better estimate the exposure risks of organic contaminants to humans in the indoor environment. The categories of the main detected chemical groups were plant natural products (n = 57), personal care products (n = 44), pharmaceuticals (n = 44), food additives (n = 43), plasticisers (n = 43), flame retardants (n = 43), colourants (n = 42) and pesticides (n = 31).</p> <p>Additional notes: For entries existing in multiple isomeric forms, an additional row has been added (ID347-352) with corresponding identifiers while the metadata for each isomer exist as per original entries<br> </p>
Long-Term Tracing of Indoor Solar Harvesting
<p><strong>Dataset Information</strong></p> <p>This dataset presents long-term term indoor solar harvesting traces and jointly monitored with the ambient conditions. The data is recorded at 6 indoor positions with diverse characteristics at our institute at ETH Zurich in Zurich, Switzerland.</p> <p>The data is collected with a measurement platform [3] consisting of a solar panel (AM-5412) connected to a bq25505 energy harvesting chip that stores the harvested energy in a virtual battery circuit. Two TSL45315 light sensors placed on opposite sides of the solar panel monitor the illuminance level and a BME280 sensor logs ambient conditions like temperature, humidity and air pressure.</p> <p>The dataset contains the measurement of the energy flow at the input and the output of the bq25505 harvesting circuit, as well as the illuminance, temperature, humidity and air pressure measurements of the ambient sensors. The following timestamped data columns are available in the raw measurement format, as well as preprocessed and filtered HDF5 datasets:</p> <ul> <li><code>V_in</code> - Converter input/solar panel output voltage, in volt</li> <li><code>I_in</code> - Converter input/solar panel output current, in ampere</li> <li><code>V_bat</code> - Battery voltage (emulated through circuit), in volt</li> <li><code>I_bat</code> - Net Battery current, in/out flowing current, in ampere</li> <li><code>Ev_left</code> - Illuminance left of solar panel, in lux</li> <li><code>Ev_right</code> - Illuminance left of solar panel, in lux</li> <li><code>P_amb</code> - Ambient air pressure, in pascal</li> <li><code>RH_amb</code> - Ambient relative humidity, unit-less between 0 and 1</li> <li><code>T_amb</code> - Ambient temperature, in centigrade Celsius</li> </ul> <p>The following publication presents and overview of the dataset and more details on the deployment used for data collection. A copy of the abstract is included in this dataset, see the file <code>abstract.pdf</code>.</p> <blockquote> <p>L. Sigrist, A. Gomez, and L. Thiele. "Dataset: Tracing Indoor Solar Harvesting." In Proceedings of the 2nd Workshop on Data Acquisition To Analysis (DATA '19), 2019.</p> </blockquote> <p><strong>Folder Structure and Files</strong></p> <ul> <li><code>processed/</code> - This folder holds the imported, merged and filtered datasets of the power and sensor measurements. The datasets are stored in HDF5 format and split by measurement position <code>posXX</code> and and power and ambient sensor measurements. The files belonging to this folder are contained in archives named <code>yyyy_mm_processed.tar</code>, where <code>yyyy</code> and <code>mm</code> represent the year and month the data was published. A separate file lists the exact content of each archive (see below).</li> <li><code>raw/</code> - This folder holds the raw measurement files recorded with the RocketLogger [1, 2] and using the measurement platform available at [3]. The files belonging to this folder are contained in archives named <code>yyyy_mm_raw.tar</code>, where <code>yyyy</code> and <code>mm</code>represent the year and month the data was published. A separate file lists the exact content of each archive (see below).</li> <li><code>LICENSE</code> - License information for the dataset.</li> <li><code>README.md</code> - The README file containing this information.</li> <li><code>abstract.pdf</code> - A copy of the above mentioned abstract submitted to the DATA '19 Workshop, introducing this dataset and the deployment used to collect it.</li> <li><code>raw_import.ipynb</code> [<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3715472/files/raw_import.ipynb">open in nbviewer</a>] - Jupyter Python notebook to import, merge, and filter the raw dataset from the <code>raw/</code> folder. This is the exact code used to generate the processed dataset and store it in the HDF5 format in the <code>processed/</code>folder.</li> <li><code>raw_preview.ipynb</code> [<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3715472/files/raw_preview.ipynb">open in nbviewer</a>] - This Jupyter Python notebook imports the raw dataset directly and plots a preview of the full power trace for all measurement positions.</li> <li><code>processing_python.ipynb</code> [<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3715472/files/processing_python.ipynb">open in nbviewer</a>] - Jupyter Python notebook demonstrating the import and use of the processed dataset in Python. Calculates column-wise statistics, includes more detailed power plots and the simple energy predictor performance comparison included in the abstract.</li> <li><code>processing_r.ipynb</code> [<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3715472/files/processing_r.ipynb">open in nbviewer</a>] - Jupyter R notebook demonstrating the import and use of the processed dataset in R. Calculates column-wise statistics and extracts and plots the energy harvesting conversion efficiency included in the abstract. Furthermore, the harvested power is analyzed as a function of the ambient light level.</li> </ul> <p><strong>Dataset File Lists</strong></p> <p><em>Processed Dataset Files</em></p> <p>The list of the processed datasets included in the <code>yyyy_mm_processed.tar</code> archive is provided in <code>yyyy_mm_processed.files.md</code>. The markdown formatted table lists the name of all files, their size in bytes, as well as the SHA-256 sums.</p> <p><em>Raw Dataset Files</em></p> <p>A list of the raw measurement files included in the <code>yyyy_mm_raw.tar</code> archive(s) is provided in <code>yyyy_mm_raw.files.md</code>. The markdown formatted table lists the name of all files, their size in bytes, as well as the SHA-256 sums.</p> <p><strong>Dataset Revisions</strong></p> <p><em>v1.0 (2019-08-03)</em></p> <p>Initial release.<br> Includes the data collected from 2017-07-27 to 2019-08-01. The dataset archive files related to this revision are <code>2019_08_raw.tar</code> and <code>2019_08_processed.tar</code>.<br> For position <em>pos06</em>, the measurements from 2018-01-06 00:00:00 to 2018-01-10 00:00:00 are filtered (data inconsistency in file <code>indoor1_p27.rld</code>).</p> <p><em>v1.1 (2019-09-09)</em></p> <p>Revision of the processed dataset v1.0 and addition of the final dataset abstract.<br> Updated processing scripts reduce the timestamp drift in the processed dataset, the archive <code>2019_08_processed.tar</code> has been replaced.<br> For position <em>pos06</em>, the measurements from 2018-01-06 16:00:00 to 2018-01-10 00:00:00 are filtered (<code>indoor1_p27.rld</code> data inconsistency).</p> <p><em>v2.0 (2020-03-20)</em></p> <p>Addition of new data.<br> Includes the raw data collected from 2019-08-01 to 2019-03-16. The processed data is updated with full coverage from 2017-07-27 to 2019-03-16. The dataset archive files related to this revision are <code>2020_03_raw.tar</code> and <code>2020_03_processed.tar</code>.</p> <p><strong>Dataset Authors, Copyright and License</strong></p> <ul> <li>Authors: Lukas Sigrist, Andres Gomez, and Lothar Thiele</li> <li>Contact: Lukas Sigrist (<a href="mailto:lukas.sigrist@tik.ee.ethz.ch">lukas.sigrist@tik.ee.ethz.ch</a>)</li> <li>Copyright: (c) 2017-2019, ETH Zurich, Computer Engineering Group</li> <li>License: Creative Commons Attribution 4.0 International License (<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>)</li> </ul> <p><strong>References</strong></p> <p>[1] L. Sigrist, A. Gomez, R. Lim, S. Lippuner, M. Leubin, and L. Thiele. <em>Measurement and validation of energy harvesting IoT devices.</em> In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017.</p> <p>[2] ETH Zurich, Computer Engineering Group. RocketLogger Project Website, <a href="https://rocketlogger.ethz.ch/">https://rocketlogger.ethz.ch/</a>.</p> <p>[3] L. Sigrist. <em>Solar Harvesting and Ambient Tracing Platform</em>, 2019. <a href="https://gitlab.ethz.ch/tec/public/employees/sigristl/harvesting_tracing">https://gitlab.ethz.ch/tec/public/employees/sigristl/harvesting_tracing</a></p>
Real-life instances of a non-commercial indoor football league
<p>This repository accompanies the paper 'Scheduling a Non-Commercial Indoor Football League: a Tabu Search Based Approach' (Van Bulck, Goossens, Spieksma (2017)). More specifically, it stores all input instances and the generated schedules.</p>
Dataset and code to reproduce analysis on the impact of indoor residual spraying (IRS) on malaria at Illovo Nchalo, Malawi
<p><strong>V3 edit: </strong>The latest R file contains extra lines of code to produce prediction intervals. </p> <p> </p> <p><strong>The repository contains:</strong></p> <p>- Excel sheets for each round of indoor residual spraying from 2014 - 2018 for villages based on the Illovo Nchalo Estate (provided by public health officer)</p> <p>- Weather data for 1999 - 2019 downloaded from Sasri Weather web for Malawi - Illovo Nchalo (Open access after signing up)</p> <p>- Explanation of variables downloaded from Sasri Weather Web</p> <p>- Expected population: number of residents living in Illovo clinic's catchment areas based on 2016 and 2019 census. Linear interpolation for the other years</p> <p>- Malaria data per month per clinic from the public health officer's records at Illovo Nchalo for 7 clinics for 2014 - 2018</p> <p>- Malaria data downloaded and selected from DHIS2 (access upon request and approval)</p> <p>- R file to reproduce figures, tables, and results for the paper under submission for PLOS GPH</p> <p>- Geopackages of data that is not open-source already to reproduce the map in figure 1</p> <p> </p> <p><strong>Description of IRS data:</strong></p> <p>- Village: Name of the villages based at Illovo being targeted for IRS</p> <p>- Target_spray: Number of structures within the village targeted for spraying</p> <p>- Sprayed: Number of structures actually sprayed</p> <p>- Date_start: Start date of the IRS campaign in a village</p> <p>- Date_end: End date of the IRS campaign in that village</p> <p>- Coverage_p: Percentage of structures sprayed calculated from "target_spray" and "sprayed"</p> <p> </p> <p><strong>Notes on reconciling the different years of IRS:</strong></p> <p>1. Post office and D. compound have been added to Nkombedzi</p> <p>2. B compound has been added to Riverside/Mess</p> <p>3. The following villages attend the following clinics</p> <p> </p> <p><strong>The following villages attend the assigned clinics:</strong><br>- Mess and Bonksville -> Factory<br>- Mlambe and Paxman -> Mangulenje<br>- Sande Ranch -> Lengwe<br>- Mechanical Pool -> Mwanza</p> <p> </p> <p><strong>Description of the malaria data:</strong></p> <p>- Date, month, year</p> <p>- Time_dummy: 1 to 48, over the study period</p> <p>- Village: The name of the village the clinic is based in. In further analyses, this is referred to as "clinic" instead to avoid confusion.</p> <p>- Total_cases: total number of cases testing positive for malaria by RDT, or in a very small percentage of cases microscopy (only used when RDT gives inconclusive or conflicting results, or when symptoms persist with negative RDT). Cases_on + cases_off = total_cases</p> <p>- Cases_on: Number of malaria cases from residents of villages located within the boundaries of the Illovo estate</p> <p>- Cases_off: Number of malaria cases from residents of villages located (just) outside the boundaries of the Illovo estate</p> <p>- Total_patients: Total number of patients attending the clinic that month</p> <p> </p> <p>From the selected control clinics only "WHO NMCP P Confirmed malaria cases" was used to indicate the number of malaria cases and "CMED Total Population" to indicate the clinic catchment population. Further info on DHIS2 website. </p> <p> </p> <p>For further information don't hesitate to contact Remy Hoek Spaans. </p> <p> </p> <p> </p> <p> </p>
Continuous Long-term Wi-Fi Fingerprinting Dataset for Indoor Positioning (full version)
<p>Database with Wi-Fi samples (RSSI measurements) collected from several Raspberry Pi (RPi) 3B+ devices continuously over 2+ years. The database includes the long-term dataset from the RPi devices (with 7,435,398 Wi-Fi samples), as well as 12 site-survey datasets (with 11,140 Wi-Fi samples) conducted in this period. The site-surveys were also conducted with a RPi 3B+.</p> <p>The measurements obtained from the RPi 3B+ Wi-Fi interface include the list of detected APs, their signal strength (RSSI) and transmission channel. The list has APs from the 2.4GHz and 5GHz bands because it supports IEEE 802.11.b/g/n/ac wireless LAN. </p> <p>These data were collected at a university building, between 19 Feb. 2019 and 25 Mar. 2021.</p> <p>The supporting material includes the Python scripts to parse and analyse the data by generating various plots. It also includes the locations of the monitoring devices and the list of reference points considered in the site-surveys.</p> <p> </p> <p>A detailed description of this dataset and the data collection process can be found here:</p> <p>Silva I, Pendão C, Moreira A. Collection of a Continuous Long-Term Dataset for the Evaluation of Wi-Fi-Fingerprinting-Based Indoor Positioning Systems. <em>Sensors</em>. <strong>2022</strong>; 22(22):8585. <a href="https://doi.org/10.3390/s22228585">https://doi.org/10.3390/s22228585</a></p> <p> </p> <p>When using this dataset, please add a citation to the paper above or this citation:</p> <p>Silva, I., Pendão, C., & Moreira, A. (2022). Continuous Long-term Wi-Fi Fingerprinting Dataset for Indoor Positioning (full version) (1.1.0) [Data set]. Zenodo. <a href="Silva, I., Pendão, C., & Moreira, A. (2022). Continuous Long-term Wi-Fi Fingerprinting Dataset for Indoor Positioning (full version) (1.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6928554">https://doi.org/10.5281/zenodo.6928554</a> </p> <p> </p> <p>The following papers have used this dataset for quantifying radio map degradation and overcoming radio map degradation in Wi-Fi fingerprinting:</p> <ul> <li>I. Silva, C. Pendão, J. Torres-Sospedra and A. Moreira, "Quantifying the Degradation of Radio Maps in Wi-Fi Fingerprinting," <em>2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN)</em>, Lloret de Mar, Spain, 2021, pp. 1-8, doi: 10.1109/IPIN51156.2021.9662558.</li> <li>I. Silva, C. Pendão, J. Torres-Sospedra and A. Moreira, "Overcoming Radio Map Degradation in Wi-Fi-based Positioning Systems," <em>2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)</em>, Nuremberg, Germany, 2023, pp. 1-6, doi: 10.1109/IPIN57070.2023.10332545.</li> </ul>
Growth of Heracleum sosnowskyi Manden. plant in indoor conditions after end of vegetation period
<p>Growth of<em> Heracleum sosnowskyi</em> Manden. plant in indoor conditions after end of vegetation period. The period of observation of plant growth from October 2017 to February 2018. The series of images.</p>
Kinematically collected reference fingerprint map (RFM) with the high precision tracking system for feature-based indoor positioning
<p>The offline referencing phase, one of the core phases of the fingerprinting-based indoor positioning system (FIPS), is the key stage for deploying the positioning system. The reference fingerprint map (RFM) is acquired for representing the relationship between location-relevant features and the corresponding locations and used for inferring the user’s location at the online stage. The kinematically collecting the RFM using the mobile device with the help of high precision tracking system is contributed to the community for benchmarking comparison of the indoor positioning performance. The detailed description of the data is cooming soon.<br> </p>
Indoor Localization Dataset
<p>The dataset contains information concerning the older people’s movement inside their homes regarding their indoor location in the home setting.</p> <p>The dataset is recorded daily with the use of smart beacon devices installed in each older person's home and monitored through the system.</p> <p>Each record of the dataset has the following fields:</p> <p>- <strong>part_id</strong>: The user ID, which should be a 4-digit number</p> <p>- <strong>ts_date</strong>: The recording date, which follows the “YYYYMMDD” format, e.g. 14 September 2017, is formatted as 20170914</p> <p>- <strong>ts_time</strong>: The recording time, which follows the “hh:mm:ss” format</p> <p>- <strong>room</strong>: The room which the person entered on the specific date and time (It is assumed that the person remained in the room till the next recording of the same day)</p>
Evaluating Open Science Practices in Indoor Positioning and Indoor Navigation Research (Supplementary Material: Full Paper Listing and Analysis)
<p>Supplementary material of the paper:</p> <p>Title: "Evaluating Open Science Practices in Indoor Positioning and Indoor Navigation Research"<br>Subtitle: "A Survey of the IPIN's Reference Papers of 2022 and 2023 Editions"</p> <p>The paper is accepted to the "14th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2024, Hong Kong, October 14-17, 2024, IEEE, 2024.</p> <p>An Author's accepted version of the manuscript is available here: <a href="../records/13684170" target="_blank" rel="noopener">https://zenodo.org/records/13684170</a> </p> <p>If you want to refer to this work, please cite this Zenodo entry as well as the published conference version.</p> <p> </p> <p>---------------------------------------</p> <p>This entry contains two files:</p> <ul> <li>"Paper Characterization Spreadsheet.xlsx": <strong>The spreadsheet of the full analysis of this work</strong>, as described in the paper. It characterizes various features of the analyzed papers and forms the raw data on which the analyses of our work were based.</li> <li>"Main features of the manuscripts analysed in Zenodo Record #12088175.pdf": A document summarizing the main features of the IPIN's Reference Papers of the 2022 and 2023 Editions, that contain some form of open resources (Open Data, Code, or Material).</li> </ul> <p> </p> <p> </p> <p> </p>
Hyperspectral environmental illumination maps for outdoor and indoor scenes
<p>This repository contains a dataset of hyperspectral illumination maps collected from 6 outdoor and 4 indoor scenes.</p> <p> </p> <p>If you use this dataset in your research, please cite:</p> <p> </p> <p>Takuma Morimoto, João M. M. Linhares, Sérgio M. C. Nascimento, and Hannah E. Smithson, “How many surfaces can you distinguish by color? Real environmental lighting increases discriminability of surface colors,” Optics Express (in press).</p> <p> </p> <p>Technical details about data acquisition are described in:</p> <p> </p> <p>Takuma Morimoto, Sho Kishigami, João M.M. Linhares, Sérgio M.C. Nascimento, and Hannah E. Smithson, “Hyperspectral environmental illumination maps: characterizing directional spectral variation in natural environments,” Optics Express, 27, 22, 32277 - 32293. (2019). <a href="https://doi.org/10.1364/OE.27.032277">https://doi.org/10.1364/OE.27.032277</a></p> <p> </p> <p>Each file includes the following formats:</p> <p> </p> <ol> <li><strong>png</strong>: RGB image for visualization.<br><br></li> <li><strong>mat</strong>: Hyperspectral image with wavelengths from 400 nm to 700 nm in 10 nm steps. Each pixel value represents spectral radiance in W m−2 sr−1 nm−1. The image and wavelength range are stored in the variables ‘radiance’ and ‘wls’, respectively.</li> </ol> <p>The images have an average spatial resolution of 1019 (height) × 2035 (width) across 10 scenes.</p> <p>File names follow the format X_sceneY, where X is the scene type (outdoor or indoor) and Y is the scene number.</p> <p> </p> <p>For Python users, the mat file can be loaded using e.g. scipy.io.loadmat. More information: <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html</a></p> <p> </p> <p>Note: To use for hyperspectral renderings (e.g., Mitsuba), convert the hyperspectral image to the OpenEXR format.</p>
Indoor Wireless Deterministic Anycast Transmissions Data from the FIT IoT-Lab testbed
<p>This dataset contains the raw openwsn results generated by indoor experiments.</p> <p>The data was collected on the <a href="https://www.iot-lab.info">FIT IoT-Lab</a> platform, using the m3 motes with a AT86RF231 radio chip, on the Grenoble's site.</p> <p>We rely on the following workflow:</p> <ul> <li>a modified version of openwsn that implements anycast transmissions at the link layer (CCA branch, <a href="https://github.com/ftheoleyre/openwsn-fw/releases/tag/duocast-mswim21">https://github.com/ftheoleyre/openwsn-fw/releases/tag/duocast-mswim21</a>). The firmware is implemented in C, and is executed by the m3 motes;</li> <li>a modified version of openvisualizer (<a href="https://github.com/ftheoleyre/openvisualizer/releases/tag/mswim21">https://github.com/ftheoleyre/openvisualizer/releases/tag/mswim21</a>)</li> <li>a tool to process the dataset and compute the metrics: end-to-end reliability, number of transmissions, CCA events, etc. (<a href="https://github.com/ftheoleyre/openwsn-data/releases/tag/mswim21-duocast">https://github.com/ftheoleyre/openwsn-data/releases/tag/mswim21-duocast</a>)</li> </ul> <p> </p> <p> </p> <p> </p>
Supplementary Materials for "Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms"
<p>This dataset was created as suplementary material for research article: <strong>Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms</strong></p> <p>This package contains packet capture files of 802.11 probe requests captured at Geotec office at University Jaume I, Spain by 5 ESP32 microcontrollers. The packet capture files are in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).</p> <p>The data are split between radio map data captured at all accessible reference positions in our office spread in 1m grid and evaluation data gathered alligned to 0.5m grid, as well as in hard to access locations. The location the data were collected are available in the office.</p> <p>The dataset has 4 parts, and all subsets of the dataset can be generated from the captured pcap files:</p> <p><strong>Data</strong></p> <p>This folder contains pcap files from all 5 ESP32 stations representing the whole radio environment map. The folder name stands for each of the 5 ESP32 sniffer stations and the name of the file points to a reference location the data were captured in. Example of the coordinates matching the reference location grid names are in following table:</p> <table> <caption>Data Point Coordinates</caption> <thead> <tr> <th scope="row"> </th> <th scope="col">X</th> <th scope="col">Y</th> <th scope="col"> </th> <th scope="col">X</th> <th scope="col">Y</th> <th scope="col"><strong>...</strong></th> </tr> </thead> <tbody> <tr> <th scope="row">A1</th> <td>0.85</td> <td>0.1</td> <td><strong>B1</strong></td> <td>1.85</td> <td>0.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A2</th> <td>0.85</td> <td>1.1</td> <td><strong>B2</strong></td> <td>1.85</td> <td>1.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A3</th> <td>0.85</td> <td>2.1</td> <td><strong>B3</strong></td> <td>1.85</td> <td>2.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">...</th> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A11</th> <td>0.85</td> <td>10.1</td> <td><strong>B11</strong></td> <td>1.85</td> <td>10.1</td> <td><strong>...</strong></td> </tr> </tbody> </table> <p><strong>Data_Eval</strong></p> <p>This folder contains pcap files from all 5 ESP32 stations with data captured at 31 locations not found in the original reference location grid. The naming corresponds to the X and Y location in which the data were collected.</p> <p><strong>Processed_Data</strong></p> <p>Additionally, there are 3 folders with processed CSV files. One folder that combines all radio map values, second folder contains combined evaluation values and third is with linearly interpolated radio map values.</p> <p>The CSV files are in a format:</p> <blockquote> <p><code>X, Y, RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5</code></p> </blockquote> <p><strong>Data_Scenarios</strong></p> <p>This folder for the ease of use, contains data for exact reproducibility of our results in the paper. There 14 scenarios described in the following table:</p> <table> <caption>Scenario Descriptions</caption> <thead> <tr> <th scope="col"> <p>Data Name</p> </th> <th scope="col"> <p>Scenario Description</p> </th> </tr> </thead> <tbody> <tr> <td>GPR00</td> <td>Only measured data, 50 samples per reference position</td> </tr> <tr> <td>GPR01</td> <td>Measured data with empty spots filled using Linear interpolation, 50 samples per reference position</td> </tr> <tr> <td>GPR02</td> <td>Gaussian Regression trained only on measured data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR03</td> <td>Gaussian Regression trained only on measured data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR04</td> <td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR05</td> <td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR06</td> <td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR07</td> <td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR08</td> <td>Gaussian Regression trained only on measured data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR09</td> <td>Gaussian Regression trained only on measured data - 0.5m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR10</td> <td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR11</td> <td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR12</td> <td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR13</td> <td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 1 sample per reference position</td> </tr> </tbody> </table> <p>The folder contains 4 files for each scenario. The Beginning of the filename corresponds to the data name, with suffix describing what data are in the file. The descriptions of used suffixes are in the following table:</p> <table> <caption>File Suffix Descriptions</caption> <tbody> <tr> <td> <p><strong>Suffix</strong></p> </td> <td> <p><strong>Suffix Description</strong></p> </td> </tr> <tr> <td>_trncrd</td> <td>Training Labels</td> </tr> <tr> <td>_trnrss</td> <td>Training RSSI Values</td> </tr> <tr> <td>_tstcrd</td> <td>Evaluation Labels</td> </tr> <tr> <td>_tstrss</td> <td>Evaluation RSSI Values</td> </tr> </tbody> </table> <p>These data are in format compatible with systems that apart from X and Y coordinates also detect, building, floor etc.</p> <p>The RSSI data are in format:</p> <blockquote> <p>RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5</p> </blockquote> <p>The Labels are in format: (Since we only use positioning in 1 office, apart X and Y coordinates are set to 0)</p> <blockquote> <p>X, Y, 0, 0, 0</p> </blockquote>
Supplementary materials for "TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density"
<p>Supplementary materials for "TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density"</p> <p> </p> <p>For more information please refer to the data descriptor available at: https://www.sciencedirect.com/science/article/pii/S2352340924003251</p> <p>Please cite as:</p> <p>Klus, L., Klus, R., Lohan, E.S., Nurmi, J., Granell, C., Valkama, M., Talvitie, J., Casteleyn, S. and Torres-Sospedra, J., 2024. TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density. <em>Data in Brief</em>, p.110356.</p> <p> </p>
Indoor Environmental Quality in Schools: NOTECH Solution vs. Standard Solution - dataset
<p>Dataset for "Indoor Environmental Quality in Schools: NOTECH Solution vs. Standard Solution"</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.