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241 results for “low-cost”
Comparative analysis of ADC values between high-cost (US331-000005-030PA) and low-cost (B07YZLCSRP) depth sensors
<p>The dataset provides a comparison between an expensive depth sensor, the "US331-000005-030PA", and a cheaper sensor, the "B07YZLCSRP". The dataset includes the ADC values from both sensors as well as the offset between them.</p> <p>Data were collected using an autonomous underwater profiler called s-Nautilus at the Real Club de Regatas de Cartagena. During this test, the s-Nautilus profiler was moved to various depths, and the time and 12-bit ADC values from both sensors were recorded.</p> <p>The recorded variables include:</p> <ul> <li><strong>timestamp UNIX (s):</strong> the timestamp indicating the date and time of each measurement.</li> <li><strong>hours (hh:mm:ss):</strong> time of recording of each measurement.</li> <li><strong>incr_time (s): </strong>cumulative time increment for each measurement.</li> <li><strong>ADC cheap sensor (unit of ADC of 12 bits): </strong>12-bit ADC values of depth sensor "B07YZLCSRP" at various depths of the s-Nautilus.</li> <li><strong>ADC expensive sensor (unit of ADC of 12 bits):</strong> 12-bit ADC values of depth sensor "US331-000005-030PA" at various depths of the s-Nautilus.</li> <li><strong>ADC difference (unit of ADC of 12 bits):</strong> difference in ADC values between the two sensors.</li> <li><strong>ADC + offset (unit of ADC of 12 bits):</strong> ADC values of depth sensor "B07YZLCSRP" adjusted by calculated offset.</li> <li><strong>average ADC differences (unit of ADC of 12 bits):</strong> average offset ADC for all measurements.</li> </ul>
Datasets of the work named Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform
<pre>- 1_Motion_Simulator/ - IMU_results/ - 20211028101756.csv - 20220114101543.csv - 20220117000000.csv - Rotary_Table/ - 15/ - rover_20220105.nav - rover_20220105.obs - solution_20220105_CAS.log - 360/ - rover_20211221.nav - rover_20211221.obs - solution_20211221_CAS.log - 360-15/ - rover_20220202_CAS.nav - rover_20220202_CAS.obs - solution_20220202_CAS.log - Static_Tests/ - solution_SSRA00CAS0 - solution_SSRA00WHU0 - 2_GNSS_Signal_Simulator/ - platformmov_C1.xtd - platformmov_C2.xtd - platformmov_C3.xtd - TestBetaNoneMov_C1 - TestBetaNoneMov_C1.nav - TestBetaNoneMov_C1.obs - TestBetaNoneMov_C1.ubx - TestBetaNoneMov_C2 - TestBetaNoneMov_C2.nav - TestBetaNoneMov_C2.obs - TestBetaNoneMov_C2.ubx - TestBetaNoneMov_C3 - TestBetaNoneMov_C3.nav - TestBetaNoneMov_C3.obs - TestBetaNoneMov_C3.ubx - 3_Test_Sea/ - 20220503000000.xlsx - solution_28.nav - solution_28.obs - solution_28.ubx Background: {Journal Article using this dataset} 'Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform' Paper DOI: <a href="https://doi.org/10.3390/s23020925">https://doi.org/10.3390/s23020925</a> Abstract: a low-cost smart sensor GNSS system has been developed to provide accurate real-time position and orientation measurements on a floating offshore wind platform. The approach chosen to offer a viable and reliable solution for this application is based on the use of the well-known advantages of the GNSS system as the main driver for enhancing the accuracy of positioning. For this purpose, the data reported in this work are captured through a GNSS receiver operating over multiple frequency bands (L1, L2, L5) and combining signals from different constellations of navigation satellites (GPS, Galileo, and GLONASS), and they are processed through the precise point positioning (PPP) and real-time kinematic (RTK) techniques. Furthermore, aiming to improve global positioning, the processing unit fuses the results obtained with the data acquired through an inertial measurement unit (IMU), reaching final accuracy of a few centimeters. To validate the system designed and developed in this proposal, three different sets of tests were carried out in a (i) rotary table at the laboratory, (ii) GNSS simulator, and (iii) real conditions in an oceanic buoy at sea. The real-time positioning solution was compared to solutions obtained by post-processing techniques in these three scenarios and similar results were satisfactorily achieved. </pre>
Supplementary Data for "Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results from a Community-Partnered Low-cost Sensor Deployment"
<p>These are the underlying data sets needed to build the kriging maps and calculate dissemination block cumulative hazard indices described in the paper. There are three data sets:</p> <ol> <li><strong>"Sampling location names and coordinates.csv"</strong>: locations and IDs of the low-cost sensors and the regulatory monitoring stations used in this work.<strong> [NOTE: </strong>latitudes and longitudes for the sensor deployments have been intentionally rounded to protect the location of volunteer sensor hosts.]</li> <li><strong>"Dissemination Block Populations.csv"</strong>: These are the relevant dissemination blocks in the study domain and their associated populations. This information was originally extracted from: https://censusmapper.ca/#13/49.2430/-123.1252</li> <li><strong>"Daily average concentrations by site and pollutant.csv"</strong>: This contains the PM2.5, NO2 and O3 daily averages for the entire study period across all low-cost sensor sites and regulatory monitoring stations. Refer to "Sampling location names and coordinates.csv" to parse the labels in this data set.</li> </ol> <p>There is also a sample code in Python to construct the kriging maps provided in 2 formats. <strong>[NOTE: </strong>we have intentionally excluded uploading the exact data sets imported by this code; our original data contains exact locations of sensor host volunteers and thus cannot be shared.]</p> <ol> <li><strong>"Jain et al - GeoHealth - Kriging Script.ipynb"</strong>: A Jupyter notebook script to import the data, build kriging maps, calculate CHIs, and export the data.</li> <li><strong>" Jain et al - GeoHealth - Kriging Script.pdf"</strong>: A PDF export of the Jupyter notebook so that you can read the Python scripts even if you are not a Jupyter notebooks user.</li> </ol>
A small dataset for analyzing spectroscopic parameters from low-cost electronic structure methods
<p>This dataset comprises two files: `lee_bayesian_dataset.csv` and `raw_outputs.tar.xz`. The former corresponds to an aggregated table of data used for subsequent analysis, and the latter are raw output files from Gaussian '09. This dataset corresponds to 6916 calculations of 76 representative molecules with high-resolution gas-phase rotational constants.</p> <p> </p> <p>This version corresponds to the data used for our publication:</p> <p>Bayesian Analysis of Theoretical Rotational Constants from Low-Cost Electronic Structure Methods</p> <p>https://pubs.acs.org/doi/10.1021/acs.jpca.9b09982</p>
A low-cost contactless overhead micrometer surface scanner (supplementary material)
<p>This is an open dataset and also the supplementary material for the paper "A low-cost contactless overhead micrometer surface scanner (supplementary material)", in the Applied Sciences journal, by the same authors.</p> <p>Abstract</p> <p>The design and implementation of a contactless scanner and its software are proposed. The scanner regards the photographic digitisation of planar and approximately planar surfaces and is proposed as a cost-efficient alternative to off-the-shelf solutions. The result is 19.8 Kppi, micrometer scans, in the service of several applications. Accurate surface mosaics are obtained based on a novel image acquisition and image registration approach that actively seeks registration cues by acquiring auxiliary images and fusing proprioceptive data in the correspondence and registration tasks. The device and operating software are explained, provided as an open prototype, and evaluated qualitatively and quantitatively.</p>
A low-cost contactless overhead micrometer surface scanner (input data)
<p>This is an open dataset and also the input data to create the supplementary material for the paper "A low-cost contactless overhead micrometer surface scanner (supplementary material)", in the Applied Sciences journal, by the same authors.</p> <p>The design and implementation of a contactless scanner and its software are proposed. The scanner regards the photographic digitisation of planar and approximately planar surfaces and is proposed as a cost-efficient alternative to off-the-shelf solutions. The result is 19.8 Kppi, micrometer scans, in the service of several applications. Accurate surface mosaics are obtained based on a novel image acquisition and image registration approach that actively seeks registration cues by acquiring auxiliary images and fusing proprioceptive data in the correspondence and registration tasks. The device and operating software are explained, provided as an open prototype, and evaluated qualitatively and quantitatively.</p>
RINEX files from low-cost GNSS receivers equipped with few types of low-cost and high-end antennas, Wrocław, Poland; July, 2022
<p>Daily RINEX files with multi-GNSS (GPS, GLONASS, Galileo) observations at 30 sec. interval obtained with low-cost GNSS receiver u-blox ZED-F9P and few types of low-cost and high-end antennas. Time period: 05.07.2022 - 18.07.2022.</p>
TURDATA: a database of low-cost air quality and remote sensing measurements for the validation of micro-scale models in the real Prague urban environments
<p><strong>README</strong></p> <p>TURDATA is a supplementary data set for the TURBAN project Prague observation campaign described in the manuscript Bauerová et al. 2024 (submitted for publication). The measurement campaign was focused on air pollution and meteorological measurement, including vertical profiles in selected part of Prague city centre called here as Legerova domain. Within this area, one professional meteorological station (MS) Prague Karlov and one reference traffic air quality monitoring (AQM) station Prague 2-Legerova (classified as traffic hotspot) are located. To gain high spatial and temporal resolution data, the supplementary measurement network was established, which consisted of:</p> <p>- 20 combined low-cost sensor (LCS) stations for monitoring of PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations (using Plantower PMS7003 particle counters and Envea Cairsense electrochemical sensors) placed in different sites and different height levels AGL (higher = H, lower = L),</p> <p>- 1 mobile telescopic meteorological mast for measuring temperature, relative humidity, wind velocity and direction and air pressure (using 2D ultrasonic anemometer Gill WindSonic 60 and weather station Gill MetConnect THP),</p> <p>- 1 MTP-5-He microwave radiometer (MWR; Attex) for temperature vertical profile,</p> <p>- 1 StreamLine XR Doppler LIDAR (HALO Photonics) for wind vertical profile. </p> <p>The main Legerova campaign lasted from 30 May 2022 to 28 March 2023 with some exceptions (see <em>TURDATA_metadata.xlsx</em> with all details). Because LCSs are known for their highly variable measurement quality, before their deployment the Legerova campaign, a sufficiently long-term initial field comparative measurement of all LCSs at RM Prague 4-Libuš was carried out (lasting from 16/12/2021 to 30/5/2022). The results showed that most of the LCSs were in raw measurement differently zero-shifted against each other and against gaseous reference or aerosol optical equivalent monitors (RMs or EMs). Therefore, the Multivariate Adaptive Regression Splines (MARS) method was applied to calculate corrected LCS concentrations based on initial field comparative measurement complemented by meteorological data from MS Prague Libuš. To check the quality of raw and MARS corrected LCS concentrations at the end of the measurement campaign, the final comparative field measurement of all LCSs at Prague 4-Libuš RM station was performed.</p> <p>Therefore, in case of LCSs measurement (both raw and corrected) the important columns of location (measurement placement: RM_Prague_4-Libus and Legerova_domain) and measurement_program (Initial_comparative_measurement, Legerova_campaign and Final_comparative_measurement) were added.</p> <p>In case of PM<sub>10</sub> and PM<sub>2.5</sub> measurement the maximum raw and MARS-corrected concentrations were influenced by temporary pollution episode on 26 July 2022 around 4 a.m. and 9 p.m. (both UTC) caused by aerosol pollution transported from large forest fire in Hřensko (the northern part of the Czech Republic). </p> <p> </p> <p>TURDATA includes the following files:</p> <p>1. <strong>TURDATA_metadata_and_photos.zip</strong> containing:</p> <p>- "<em>TURDATA_metadata.xlsx</em>" with the important list of metadata about devices placement, locations parameters and measurement periods</p> <p>- Folder "<em>Photos_from_Legerova_campaign</em>" with photos from Legerova measurement campaign</p> <p>2. <strong>AQ_LCSs_raw_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> raw measured concentrations by all LCSs</p> <p>- "<em>O3_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM10_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM2_5_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> raw measured concentrations by all LCSs</p> <p>- "<em>AQ_LCSs_raw_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>3. <strong>AQ_data_RM_stations_Prague_TURDATA.zip</strong> containing:</p> <p>- "<em>AQ_data_Prague_RM_stations_TURDATA_12-2021_06-2023.xlsx</em>" with air quality data measured by reference AQM stations in Prague</p> <p>- "<em>AQ_data_RM_stations_Prague_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>4. <strong>Meteo_data_Prague_MS_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_12-2021_06-2023.xlsx</em>" with meteorological data measured by professional meteorological stations in Prague</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>5. <strong>AQ_LCSs_MARS-corrected_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_COR_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>O3_COR_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM10_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM2_5_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>AQ_LCSs_MARS-corrected_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use and brief description of MARS correction method</p> <p>6. <strong>Meteo-mast_PVK_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo-mast_PVK_TURDATA_06-2022_06_2023.xlsx</em>“ with non-referential meteorological data measured by mobile meteo-mast</p> <p>- "<em>Meteo-mast_data_PVK_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>7. <strong>MWR_temperature_profile_TURDATA.zip</strong> containing:</p> <p>- "<em>MWR_5min_temperature_TURDATA_02-2022_03-2023.xlsx</em>" with raw temperature vertical profile measurement from microwave radiometer</p> <p>- "<em>MWR_1hour_temperature_TURDATA.xlsx</em>" with 1-hour averaged temperature vertical profile from microwave radiometer</p> <p>- "<em>MWR_1hour_TMP_gradient_TURDATA.xlsx</em>" with 1hour temperature gradient calculated from raw temperature profiles measured by microwave radiometer</p> <p>- "<em>MWR_temperature_profile_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>8. <strong>LIDAR_wind_profile_TURDATA.zip</strong> contains:</p> <p>- Individual folders "yyyymm“ -> "yyyymmdd"</p> <p>- Each daily folder "yyyymmdd" contains files:</p> <p>a) "<em>Processed_Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with processed WV and WS data</p> <p>b) "<em>Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with non-processed Doppler wind profile data</p> <p>- "<em>LIDAR_wind_profile_TURADATA_readme.txt</em>" with all necessary information for correct data use</p>
Development of a Low-Cost Method for Quantifying Microplastics in Soils and Compost Using Near-Infrared Spectroscopy
<p>Datasets and scripts for data evaluation</p>
Flat-Field Super-Resolution Localization Microscopy with a Low-Cost Refractive Beam-Shaping Element
<p>Raw data for the article "Flat-Field Super-Resolution Localization Microscopy with a Low-Cost Refractive Beam-Shaping Element". Each set of three files is a set of dSTORM images, taken using either top-hat illumination or a Gaussian illumination. For Sample 0, the top-hat illumination was performed first. For Sample 1, the Gaussian illumination was performed first.</p>
Software and data for: VGWatch: A low-cost stand-alone monitor to measure the valve gape of bivalves in the field
<p>Software and data abstract:</p> <p>This archive includes an Arduino script for utilizing the valve gape monitor outlined in the paper. Additionally, it provides an R script for calibrating the monitor and magnet. Finally, it contains valve gape data from three mussels, collected using the described monitor, along with a script for the analysis.</p> <p> </p> <p>Paper abstract:</p> <p>Valve gape monitors are utilised to measure the extent of bivalve’s shell openings. This measurement is correlated with their filter-feeding activity, and valve closure can serve as an indicator of various environmental stressors, including poor water quality, the presence of predators, and human disturbance. We developed an inexpensive, stand-alone, and reusable valve gape monitor; enabling collection of sufficient and independent spatial replication <em>in situ</em>. The monitor is made using Arduino hardware and software, and we share the design and code. We tested it in the field with three mussels as a proof of concept. The mussels were likely actively feeding most of the time. Two of the mussels were in close vicinity and demonstrated similar valve gape patterns. The monitor enables to measure valve gape in high resolution, providing insights into bivalve biology, ecosystem functioning, and the effects of environmental conditions, including anthropogenic stressors.</p>
Data from: Evaluation of a low-cost staining method for improved visualization of sweet potato whitefly (Bemisia tabaci) eggs on multiple crop plant species
<p>The sweet potato whitefly (Bemisia tabaci) is a damaging insect pest that feeds on hundreds of crop plants. Oviposition rate is a useful metric to screen plants for whitefly resistance. Whitefly eggs are small and translucent, and can therefore be hard to count on the leaves of some crops. In this research, we tested a selective egg staining process on five crop species to determine if egg staining can improve the visualization and quantification of whitefly eggs. By comparing the egg counts before and after staining using two-sample Wilcoxon signed-rank tests (a non-parametric test for paired analyses). Two individuals counted the eggs, and for both these counters we found a significant increase in the number of visible eggs after staining on melon, tomato, and cowpea. This method could be applied to improve phenotyping for whitefly resistance in plant breeding applications.</p>
Stationary comparison data and analysis between a new low-cost meteorological device and the Technical University of Dresden Chair of Meteorology's backpack meteorological device
<p>This dataset provides stationary comparison data which was used to demontrate the suitability of a new low-cost and user-friendly meteorological device for the purpose of thermal comfort mapping. The new device was compared to an established high-end backpack-mounted device from the Dresden University of Technology (TUD) Chair of Meteorology, Germany. The main sensors for comparison were: the low-cost SHT 85 Sensirion sensor vs. the high-cost WXT520 for air temperature and relative humdity and the low-cost SR2AD pyranometer vs. the high-cost SKS 1110 pyranometer.</p>
Stationary comparison data and analysis between a new low-cost meteorological device and the MaRTy device
<p>This dataset provides stationary comparison data which was used to demontrate the suitability of a new low-cost and user-friendly meteorological device for the purpose of thermal comfort mapping. The new device was compared to the established high-end MaRTy device developed by Arizona State University's Sensable Heatscapes and Digital Environments (SHaDE) lab. The main sensors for comparison were: the low-cost SHT 85 Sensirion sensor vs. the high-cost HC2S3 Rotronic HygroClip2 for air temperature and humidity. However, the main purpose of the analysis was a comparison of the ability to predict Mean Radiant Temperature (MRT) as an essential component of thermal comfort. MRT for the low-cost device was calculated using the RayMan Pro software, while MRT for the MaRTy device is an output calculated directly by the device. </p>
Mobile comparison data and analysis between a new low-cost meteorological device and the Technical University of Dresden Chair of Meteorology's backpack meteorological device
<p>This dataset provides mobile comparison data from Tharandt and Dresden, Germany, which was used to demontrate the suitability of a new low-cost and user-friendly meteorological device for the purpose of thermal comfort mapping. The new device was compared to an established high-end backpack-mounted device from the Dresden University of Technology (TUD) Chair of Meteorology, Germany. The main sensors for comparison were: the low-cost SHT 85 Sensirion sensor vs. the high-cost WXT520 for air temperature and relative humdity and the low-cost SR2AD pyranometer vs. the high-cost SKS 1110 pyranometer. The ability of each device to predict the Universal Thermal Climate Index (UTCI), calculated using the software RayMan Pro, was also compared.</p>
Supplementary Data for "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" (abridged version)
<p>This is a supplementary data set associated with the publication "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" from the Center for Atmospheric Particle Studies, submitted to Atmospheric Measurement Techniques. This is an abbreviated version which does not include the calibrated models; these models must be re-generated by running the codes contained with the data set.</p> <p> </p>
ScintPi: A low-cost, easy-to-build GPS ionospheric scintillation monitor for DASI studies of space weather, education, and citizen science initiatives
<p>These data sets contain ionospheric scintillation (GPS L1) observations (S4 indices) collected by a ScintPi prototype during 2018 at a low magnetic latitude station (Presidente Prudente). ScintPi is a low-cost, easy-to-build GPS ionospheric scintillation monitor for DASI studies of space weather, education, and citizen science initiatives.</p> <p>The file named "ScintPi_PPR_S4_2018.mat" contains S4 values (s4mat) for 2018 as a function of universal time (utmat), day-of-year (doymat), GPS satellite identifier number (prnmat), GPS satellite elevation (elmat) and azimuth (azmat) angles.</p> <p>The file named "ScintPi_PPR_20180211.mat" contains example raw (10 Hz) measurements made by ScintPi on February 11, 2018. The file contains values of receiver's altitude, latitude and longitude (variables alt,lat, and lon), GPS satellite azimuth and elevation (variables el and az), GPS identifier number (prn), signal-to-noise ratio (snr), and day-of-year (doy).</p>
Data set supporting journal article: Markwitz, C. and Siebicke, L.: "Low-cost eddy covariance: a case study of evapotranspiration over agroforestry in Germany", Atmos. Meas. Tech., 2019
<p>This data set contains evapotranspiration data obtained by a conventional eddy covariance set-up and a low-cost eddy covariance set-up as described in the research article: Markwitz, C. and Siebicke, L.: "Low-cost eddy covariance: a case study of evapotranspiration over agroforestry in Germany", Atmos. Meas. Tech., 2019.</p> <p>The data set contains all necessary data needed to replicate figures and analysis presented in the research article. The data sets are sorted and named according to the figure the data were used for. </p>
Figures, plotting scripts, and data for "A fast, low-cost, and stable memory algorithm for implementing multicomponent transport in direct numerical simulations"
<p>This dataset contains the figures, as well as the necessary plotting scripts and data to reproduce them, for the article "A fast, low-cost, and stable memory algorithm for implementing multicomponent transport in direct numerical simulations" by Aaron J. Fillo, Jason Schlup, Guillaume Beardsell, Guillaume Blanquart, and Kyle E. Niemeyer (2019). In addition, the code used to generate the eigenvalues in Table 1 is included.</p> <p>The scripts were run in Matlab 2019a, though none of the versions used should be version-dependent. Furthermore, non-standard functions are included with dependencies hard-coded. We used export_fig (https://github.com/altmany/export_fig) to generate high-quality figures, and redistribute the version used here for reproducibility (export_fig was developed by Oliver J. Woodford and Yair M. Altman, and made available openly under the BSD 3-Clause License).</p> <p>The code included in this dataset is released under the BSD 3-Clause License (see LICENSE.txt for details), other than the source of export_fig, as described. The figures are shared under the Creative Commons Attribution 4.0 International License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).</p>
Data and scripts for the paper Building a Low-cost UAV-based LiDAR Sensor for Landscape Archaeology
<p>This repository contains the modified OpenMMS scripts for Linux and Raspberry Pi firmware for LiDAR sensor presented in the paper Building a Low-cost UAV-based LiDAR Sensor for Landscape Archaeology at the CAA 2024 conference in Auckland, New Zealand. Included are the LiDAR and trajectory data collected at the site of Antiochia ad Cragum in 2022 in an area roughly north-east of what is known as the Small Bath Area. Each zip file contains two adjacent flights oriented either principally east-west or north-south. The four flights cover the same area in an overlapping pattern.</p> <p>The LiDAR and trajectory data are released under the Creative Commons Attribution 4.0 International license and the modified OpenMMS firmware and scripts are released under the original GNU GPL v3.0 or later license.</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.