Find research datasets worth reusing

Available Wireless Sensor Network and Internet of Things testbed facilities: dataset

<p>In this data set, we present data collected for the purpose of carrying out a systematic review of the available Wireless Sensor Network and Internet of Things testbed facilities. The data was collected through multiple stages and in each stage the pre-defined criteria were applied. We provide a dataset describing the hardware and software aspects of Wireless Sensor Network and Internet of Things testbed facilities available in the market and scientific community. The data were gathered through an extensive systematic review process of scientific articles published between the years 2011 and 2021. The review aims to obtain good quality data for people who are actively researching the Internet of Things facilities or anyone who is interested in that field.</p>

Raw data for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor" (Part. 1)

<p>Raw data for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor" (Part. 1). For usage, please refer to https://github.com/freemercury/Widefield_wavefront_sensor.</p>

Raw data for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor" (Part. 2)

<p>Raw data for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor" (Part. 2). For usage, please refer to https://github.com/freemercury/Widefield_wavefront_sensor.</p>

Force/Torque Sensor Measurements for Estimating the Mass Center of an Unknown Robot End Effector

<h1>Introduction</h1> <p>This dataset was created as part of a study on a novel geometric method to estimate the mass center of an unknown robot end effector. A conference paper from this study was accepted for the 2024 IEEE International Conference on Real-time Computing and Robotics (RCAR 2024) [1]. </p> <p>A force/torque sensor (FTS) was attached to the flange of a serial robot, and an unknown end effector was attached to the FTS. Vougioukas [2] described a method to calculate the FTS bias, as well as the mass and mass center using Least Squares Estimation (LSE). His method requires FTS samples from 24 specific orientations of the sensor. See his paper for a description of this calibration method. This dataset was used to evaluate and compare the estimates from the proposed geometric technique to the estimates from Vougioukas' method. </p> <p>The hardware used to generate this dataset were:</p> <ul> <li>KUKA Agilus KR6 R900 sixx (KUKA AG, Germany)</li> <li>ATI Gamma FTS (ATI Industrial Automation, Inc., USA)</li> <li>ATI Netbox (ATI Industrial Automation, Inc., USA)</li> </ul> <h1>Dataset</h1> <p>The robot was used to move the FTS with high precision and accuracy as required by the calibration method from Vougioukas. Each line in the dataset is the measured force and torque, the direction of gravity in the FTS frame, and the orientation of the FTS expressed in the world frame. The lines are ordered and correspond to the orientations described by Vougioukas in his paper. </p> <p><strong>fx,</strong> <strong>fy, fz</strong> - The force components as measured by the FTS.<br><strong>tx, ty, tz </strong>- The torque components as measured by the FTS.<br><strong>gx,gy,gz </strong>- The direction of the gravitational vector in the FTS frame.<br><strong>r11, r12, r13, r21, r22, r23, r31, r32, r33 </strong>- The components of the rotation matrix that represents the FTS orientation in the world frame.</p> <h1>References</h1> <p>[1] A. Skrede, "A Geometric Perspective on Moment Arm Estimation Using Force/Torque Sensors", Accepted for the 2024 IEEE International Conference on Real-time Computing and Robotics (RCAR), Ålesund, Norway, June 2024 </p> <p>[2] S. Vougioukas, “Bias Estimation and Gravity Compen- sation For Force-Torque Sensors,” in Recent Advances in Simulation, Computational Methods and Soft Computing. WSEAS Press, 2001, pp. 82–85. </p>

A full year of sensor data regarding a smart building room

<p>This dataset concerns a full year of clean data regarding a room in a smart building. The data considers the following: Outside temperature (x10)(ºC), Temperature 103 (x10)(ºC), Humidity 103 (x10)(%), Heat Index 103 (x10)(ºC), Occupation, AC status 103 (bool).</p> <p>The columns idenfied by x10 indicate that their value was multiplied by 10, to observe the raw value, please divide it by 10.</p>

opencc-by-nc-sa-4.0May 2024View details →

Smart Poqueira: Predicting Rural Parking Lot Feasibility with Sensor-Questionnaire Integration

<p>We introduce a dataset comprising 525 instances of visitor behavior data from Pampaneira, Bubión, and Capileira in the Sierra Nevada National Park, Granada, Spain. Collected in January, March, and July 2023, the questionnaires excluded locals and residents. Conducted in parking lots, the questionnaires gathered information like license plate numbers, residential postcodes, visit frequency, and overnight stays. Additionally, data from four Hikvision license plate recognition (LPR) cameras tracking vehicle movement in each village during the same period supplement the dataset, enhancing understanding of individual mobility patterns. To further enrich the dataset, contextual details such as holiday days, vehicle provenance, and socio-demographic information, aiding in the validation and enhancement of questionnaire-derived data.</p> <p>The dataset comprises 26 variables, including: total_distance, nights, visits_dif_weeks, visits_dif_months, fidelity, total_entries, avg_nights, std_nights, total_holiday, avg_holiday, std_holiday, total_workday, avg_workday, std_workday, total_high_season, avg_high_season, std_high_season, total_low_season, avg_low_season, std_low_season, entry_in_holiday, entry_in_high_season, population, avg_gross_income, km_to_area, and park_price_will_affect_behaviour.</p>

Data from DHT22 a sensor humidity and temperature

<p>data from DHT22 a sensor humidity and temperature</p>

Derived environmental temperatures at Jezero crater from Air Temperature Sensors' measurements on the Perseverance rover.

<p><strong>Material from Version 2</strong> extends derived Air Temperature Sensor data to the first 700 sols of the Mars 2020 mission used in the analysis of <em>Munguira et al. (2024). "One Martian Year of Near-Surface Temperatures at Jezero from MEDA measurements on Mars2020/Perseverance". Journal of Geophysical Research: Planets. [in revision]. </em>We also include the tables needed to generate and reproduce the figures in the paper. Most importantly, the tables include the results from different analyses of temperatures through Fourier series and Reynolds averaging. </p>

Mobile Sensor Readings of Activities

<p>This collection contains mobile sensor readings used to train an activity recognition classifier. The sensor data was accessed via the Generic Sensor API. Mobile sensor readings from the following sensors:</p> <ul> <li>accelerometer</li> <li>gyroscope</li> <li>magnetometer</li> <li>gravity sensor</li> <li>relative orientation sensor</li> <li>absolute orientation sensor</li> <li>linear acceleration sensor</li> </ul> <p>Recording were measured while the following activities were measured:</p> <ul> <li>Lying</li> <li>Sitting</li> <li>Standing</li> <li>Walking</li> <li>Stairs up</li> <li>Stairs down</li> <li>Running</li> <li>Taking the car</li> <li>Taking the bus</li> <li>Taking the tram</li> <li>Taking the train</li> <li>Taking the metro</li> </ul> <h3>Structure of the dataset</h3> <p>The collected data in each recording includes the start time timestamp readingID, the activity label activity, the duration<br>of the recording in elapsedTime, and an object sensorData, which contains the lengthy sensor measurements. The measurements for each sensor is stored in the form of an array containing on each index a series of four values. The time of when the measurement was taken t, and the value in the three axes x,y and z.</p> <p>A minimalistic example of the format of the collected data:</p> <p>{<br>"readingID": 1710151914418,<br>"activity": ""walk"",<br>"elapsedTime": 17312<br>"sensorData": {<br>"acce": [<br>...<br>{<br>"t": 1624,<br>"x": 0.05,<br>"y": -0.61,<br>"z": 0.21,<br>},<br>... ]<br>"gyro": Array (844)<br>"magnet": Array (136)<br>}<br>}</p> <p> </p> <p>A big thanks to all the volunteers who helped collect this dataset.</p> <p> </p>

opencc-zeroJun 2024View details →

dryad36/100

Assessing the potential of camera traps for estimating activity pattern compared to collar-mounted activity sensors: A case study on Eurasian lynx (Lynx lynx) in South-Eastern Norway

<div> <div> <div> <div> <p>The diel activity patterns of animals convey information about physiology, ecological niches and animal behaviour relevant for both applied conservation and more theoretical research. However, these patterns are challenging to study in the field. The current gold-standard approach to quantify the movements and activity patterns of medium to large wildlife species is to use Global Positioning Systems (GPS) collars equipped with activity sensors (e.g., accelerometers). A more recent approach consists of inferring activity patterns from the time-stamped pictures of wildlife obtained from the camera traps now routinely used in wildlife monitoring projects. However, few studies have attempted to validate estimates of activity patterns obtained from camera traps against those obtained from activity sensors. In this study, we compared the diel activity pattern of the Eurasian lynx Lynx lynx inferred from detections by a network of over 300 camera traps active between 2010 and 2020, to activity patterns obtained from 18 GPS-collared lynx (8 females, 10 males) equipped with 2-axis accelerometer sensors, in the same area of southern Norway. Our results suggest that camera traps can be used to estimate diel activity curves that are comparable to those obtained from accelerometers. In our study 75 detections were sufficient to approximate the diel activity pattern obtained from accelerometer. Subsampling indicated that a low number of detections results in a coarser approximation of the diel activity pattern.</p> </div> </div> </div> </div>

BreizhSR: multi-temporal cross-sensor super-resolution of satellite imagery

<h1>BreizhSR, a super-resolution Sentinel-2 to SPOT-6/7 dataset </h1> <h2>1. Dataset motivation</h2> <p><strong>BreizhSR</strong> is a dataset targetting super-resolution of (RGB bands of) Sentinel-2 images by providing time series colocated in space and time with SPOT-6/7 acquisitions. This dataset is composed of cloud free Sentinel-2 time series (visible bands at 10m resolution) and SPOT-6/7 pansharpened color images resampled 2.5m resolution. The study area is the region of Brittany (Breizh in the local language), located on the northwestern coast of France with an oceanic climate. The dataset covers about 35 000 km² with mostly agricultural areas (about 80 %). All acquisitions are from 2018 in the Brittany region of France.</p> <h2>2. Dataset organization</h2> <p>The dataset folder follows the structure detailed below :</p> <p><code>BreizhSR</code><br><code>├── dataset_test.pkl</code><br><code>├── dataset_train.pkl</code><br><code>├── README.md</code><br><code>├── x</code><br><code>├── x_test</code><br><code>├── y</code><br><code>└── y_test</code></p> <p>The <code>README.md</code> file contains the same information as this description.</p> <p>Actual image patches are stored in the <code>x</code> and <code>x_test</code> folders for Sentinel-2 patches, and in the <code>y</code> and <code>y_test</code> folders for ground truth SPOT patches. Subfolders are organized using a integer identifier (e.g. <code>8355</code>) that denote the series identifier. Therefore, for the S2 series <code>x/8355</code>, the corresponding SPOT patch is in subfolder <code>y/8355</code>.</p> <p>This organization and additional metadata are described in two <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html">Pandas Dataframes</a> : <code>dataset_train.pkl</code> and <code>dataset_test.pkl</code>. These files are Dataframes serialized using the <a href="https://docs.python.org/3/library/pickle.html">pickle Python serialization protocol</a>. The columns available in these Dataframes are described in the table below.</p> <table> <tbody> <tr> <td>x</td> <td>y</td> <td>wkt</td> <td>spot6_name</td> <td>sen2_acquisitions</td> <td>dates_sen2</td> <td>dates_spot6</td> <td>split</td> </tr> <tr> <td>Latitude of the center point (expressed in Lambert 93 CRS)</td> <td>Longitude of the center point (expressed in Lambert 93 CRS)</td> <td>Area of interest geometry in well-known text format</td> <td>Path to the SPOT ground truth</td> <td>Paths to the Sentinel-2 input series</td> <td>Acquisition dates for the Sentinel-2 images</td> <td>Acquisition date for the SPOT ground truth</td> <td>`train` or `test`</td> </tr> </tbody> </table> <h2>3. Data collection and preprocessing</h2> <h3>Sentinel-2</h3> <p>Sentinel-2 constellation has twin satellites launched by the European Space Agency (ESA) in 2015 and 2017 that cover all Earth’s surfaces every five days at the equator. Level-2A images of the BreizhSR dataset are gathered via the THEIA platform, which employs the MAJA pre-processing algorithm to obtain atmospherically corrected ground reflectance. To match the SPOT-6 spectral characteristics, only RGB bands at a 10-meter spatial resolution (B4, B3,and B2) are used in the analysis. The images were collected for the nine tiles covering the Brittany region from the 1st of April 2018 to the 31st of August 2018, filtering images with a cloud cover under 5 %. Since the SPOT-6 data was acquired in the summer of 2018, the Sentinel-2 time period was chosen to include images from before and after the SPOT-6 acquisitions while staying in a range of similar seasonal and climate conditions.</p> <p>Sentinel-2 tiles are cropped into 3x74x74 patches. The dataset is preprocessed with a min-max normalization, using the 2% and 98% percentile as an estimation of minimum and maximum values of Sentinel-2 data to take into account the presence of outliers due to artifacts such as clouds and their shadows.</p> <h3>SPOT-6/7</h3> <p>Orthorectified SPOT data under the Licence Ouverte is collected from the <a href="https://openspot-dinamis.data-terra.org/">DINAMIS</a> platform. Multispectral images at 6m resolution are pansharpened using the panchromatic 1.5m reference using the RCS algorithm <a href="https://www.orfeo-toolbox.org/CookBook/Applications/app_BundleToPerfectSensor.html">Orfeo ToolBox</a>, similar to the Brovey pansharpening algorithm. The pansharpened tiles are preprocessed with a min-max normalization, downsampled at 2.5m resolution and patches are finally cropped with dimensions 3x296x296.</p> <h2>4. License</h2> <p>SPOT images and the Sentinel-2 Theia L2A products are released under the <a href="https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf">Licence Ouverte 2.0</a> from the French government. This dataset contains modified Coprnicus Sentinel data from 2018, made available under free access by EU law. Other files in the dataset are licensed under Creative Commons Attribution 4.0 (CC BY 4.0).</p> <h3>Acknowledgements</h3> <p>We thank the support of GDR IASIS for funding this work under the SESURE project, the DINAMIS consortium, CNES/Airbus and IGN for access to the SPOT-6 data, and ESA for access to Sentinel-2 data. During the conduct of this research, Simon Donike received a European scholarship to engage in Master Copernicus in Digital Earth, Erasmus Mundus Joint Master Degree (EMJMD). We thank Dirk Tiede (Uni. Salzburg) for his help and feedback on BreizhSR. This work was performed using HPC resources from GENCI–IDRIS (grant 2022-AD011013003).</p>

Fig. 2 in Microenergy harvester for remote ocean buoys using piezoelectric sensors coupled with superballs

Fig. 2 — Sketch of the Piezoelectric Energy Harvester (PEH)

Fig. 8 in Microenergy harvester for remote ocean buoys using piezoelectric sensors coupled with superballs

Fig. 8 — Generated voltage with respect to the changing wave period from 1.22 to 2.13 s

Fig. 1 in Microenergy harvester for remote ocean buoys using piezoelectric sensors coupled with superballs

Fig. 1 — Schematic representation of Piezoelectric Energy Harvester (PEH) system

Fig. 3 in Microenergy harvester for remote ocean buoys using piezoelectric sensors coupled with superballs

Fig. 3 — Sketch of flume (not to scale)

Lower-body Inertial Sensor and Optical Motion Capture Recordings of Walking and Running

<pre>This dataset contains lower-body inertial sensor (IMU) data and optical motion capture (OMC) data from ten participants walking and running overground at different speeds. <br><br><br>The data recording is described in this publication: Dorschky, E., Nitschke, M., Seifer, A. K., van den Bogert, A. J., & Eskofier, B. M. (2019). Estimation of gait kinematics and kinetics from inertial sensor data using optimal control of musculoskeletal models. Journal of biomechanics, 95, 109278. (https://doi.org/10.1016/j.jbiomech.2019.07.022)<br><br>Please look at the README.txt file for further information.</pre>

Magnetic Hair Tactile Sensor for Directional Pressure Detection

<p><span>Tactile sensing in the human body is achieved via the skin. This has inspired the fabrication of synthetic skins with pressure sensors for potential applications in robotics, bio-medicine, and human-machine interfaces. Tactile sensors based on magnetic elements are promising as they provide high sensitivity and a wide dynamic range. However, current magnetic tactile sensors mostly detect pressures of solid objects and operate at relatively high forces about 100 mN. Here, we address these limitations by manufacturing soft, stretchable, and hair-like structures that are permanently magnetized to achieve high-resolution, cost-effective, and high-resolution pressure sensing. Combining these hair-like structures with advances in 3D magnetic-field measurements allows us to monitor directional tactile pressures without solid contact. To prove the concept of this technology, we built a bio-inspired soft device with a hairy structure that senses and reports environmental mechanical stresses, similar to that of human skin. Simple self-assembly of the soft magnetic hair structure makes our approach easy to scale for large-area applications.</span></p>

Source data - Engineering Modular and Tunable Single Molecule Sensors by Decoupling Sensing from Signal Output

<p>Research data supporting the findings of "<em>Engineering Modular and Tunable Single Molecule Sensors by Decoupling Sensing from Signal Output</em>" by Lennart Grabenhorst, Martina Pfeiffer, Thea Schinkel, Mirjam Kümmerlin, Gereon A. Brüggenthies, Jasmin B. Maglic, Florian Selbach, Alexander T. Murr, Philip Tinnefeld and Viktorija Glembockyte. For questions concerning this data, please reach out to Philip Tinnefeld or Viktorija Glembockyte.</p>

Raw temperature measurements from SmartSantander sensors reported between January 1st 2021 and July 31st 2022

<p>This is a smart city domain dataset, and more specifically a environmental one generated within the framework of the SmartSantander research testbed.</p> <p>It contains raw temperature measurements reported by SmartSantander sensors deployed in the spanish city of Santander, covering a period of 17 months between January 1st 2021 and July 31st 2022, and comprising more than 24 million data points. The dataset includes not only the temperature dimension but also spatial and temporal information, as well as the specific device identifier and some labels to differentiate between static/mobile and indoor/outdoor devices.</p> <p>As is common with large-scale sensor deployments, there are occasional sensor malfunctions, which have deliberately not been filtered out of this raw dataset.</p>