Find research datasets worth reusing

Reconstructed and measured bathymetry of the Brandka Pond (Bytom, S Poland) - database

The Brandka Pond belongs to anthropogenic lakes, one of many within the Upper Silesian Anthropogenic Lake District. It developed in the early 1990s in Bytom (Southern Poland) due to land subsidence and a permanent change in the area's water conditions. The database contains material on the reconstruction of the bottom relief of the Brandka Pond in Bytom based on the rate of land subsidence after coal mining. It also includes changes in the extent of the reservoir in 1993-2019 and land use between 1881 and 2019.

opencc-zeroAug 2023View details →

dryad40/100

Data for: Direct numerical simulation surface layer and IR-based measurements

This is the primary dataset used in a manuscript in the process of being submitted to JTECH. The bulk of the dataset is a direct numerical simulation (DNS) of an open channel flow with a shear-free surface. The DNS includes scalar, velocity, and divergence fields over 181 realizations. From the scalar field, another vector field was generated by a method called feature image velocimetry that successively cross-correlates the scalar fields to derive the underlying velocity field. The two velocity fields are then compared by their spectra, one from the DNS and the other derived from the scalar field.

opencc-by-4.0Dec 2021View details →

A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements

Mobile networks have become highly complex systems. In order to better understand how network features affect performance and suggest additional improvements, it is crucial to examine them from an empirical perspective. In the following, we present a large-scale dataset of measurements collected over fourth generation (4G) and fifth generation (5G) operational networks, providing Long Term Evolution (LTE), Narrowband Internet of Things (NB-IoT) and 5G New Radio (NR) connectivity. We collected our dataset during a period of seven weeks in Rome, Italy, by performing several tests on the infrastructures of two major mobile network operators (MNOs). The open-sourced dataset has enabled multi-faceted analyses of network deployment, coverage, and end-user performance, and can be further used for designing and testing artificial intelligence (AI) and machine learning (ML) solutions for network optimization tasks. If you use our dataset in your research, we kindly request that you cite the following paper: K. Kousias et al., "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," in IEEE Communications Magazine, vol. 62, no. 5, pp. 44-49, May 2024, doi: 10.1109/MCOM.011.2200707.

Measured data of global fractional vegetation cover from 2013-2021 and algorithm code for calculating remote sensing products

These data come from "A new computationally efficient algorithm to generate global fractional vegetation cover from Sentinel-2 imagery at 10m resolution", these include: 1.  Measured data of global fractional vegetation cover from 2013-2021  2.   Algorithm code for calculating fractional vegetation cover, these codes are written by JavaScript in GEE (Google Earth Engine).

Dilatometer measurements from the projects STIMTEC and STIMTEC-X

Dilatometer measurements from various of the boreholes in Reiche Zeche (Freiberg, Germany) during the projects STIMTEC and STIMTEC-X

PsPM-AOB_UW: Eye tracker (including pupillometry) measurements from an auditory oddball and a luminance task

This dataset includes eye tracker (including pupillometry) measurements from an auditory oddball task with an ITI of 2 s (session 1) and a luminance task in which discs with different shades of grey were presented. Also included are task information, keypress responses, keypress response times and key correctness for the oddball task. Data come from 23 healthy unmedicated female participants aged 41.87 +/- 3.9 years as a control group for a lesion patient with Urbach-Wiethe syndrome. Stimuli consist of sine tones (50-ms length; 10-ms ramp; 440 or 660 Hz).

Data associated with the following publication: "A fully automated measurement system for the characterization of micro thermoelectric devices near room temperature"

Data associated with the following publication: "A fully automated measurement system for the characterization of micro thermoelectric devices near room temperature" (DOI: <a href="https://doi.org/10.1016/j.applthermaleng.2023.120111">10.1016/j.applthermaleng.2023.120111</a>).

opencc-by-nc-4.0Aug 2023View details →

Logistic Activity Recognition Challenge (LARa Version 03) – A Motion Capture and Inertial Measurement Dataset

LARa Version 03 is a freely accessible logistics-dataset for human activity recognition. In the “Innovationlab Hybrid Services in Logistics” at TU Dortmund University, two picking and one packing scenarios with 16 subjects were recorded using an optical marker-based Motion Capturing system (OMoCap), Inertial Measurement Units (IMUs), and an RGB camera. Each subject was recorded for one hour (960 minutes in total). All the given data have been labelled and categorised into eight activity classes and 19 binary coarse-semantic descriptions, also called attributes. In total, the dataset contains 221 unique attribute representations. The dataset was created according to the guideline of the following paper: “A Tutorial on Dataset Creation for Sensor-based Human Activity Recognition”, PerCom, 2023 DOI: <a href="http://dx.doi.org/10.1109/PerComWorkshops56833.2023.10150401">10.1109/PerComWorkshops56833.2023.10150401</a> The LARa Version 03 contains a new Annotation tool for OMoCap and RGB Videos, namely, the Sequence Attribute Retrieval Annotator (SARA). SARA, developed and modified based on the LARa Version 02 annotation tool, includes desirable features and attempts to overcome limitations as found in the LARa annotation tool. Furthermore, few features were included based on the explorative study of previously developed annotation tools, see journal. In alignment with the LARa annotation tool, SARA focuses on OMoCap and video annotations. However, it is to be noted that SARA was not intended to be a video annotation tool with features such as subject tracking and multiple subject annotations. Here, the video is considered to be a supporting input to the OMoCap annotation. We would recommend other tools for pure video-based multiple-human activity annotation, including subject tracking, segmentation, and pose estimation. There are different ways of installing the annotation tool: Compiled binaries (executable files) for Windows and Mac can be directly downloaded from here. Python users can install the tool from https://pypi.org/project/annotation-tool/ (PyPi): “pip install annotation-tool”. For more information, please refer to the “Annotation Tool - Installation and User Manual”. Upgrade: <ul> <li>Annotation tool (SARA) added (for Windows and MacOS, including an installation and user manual)</li> <li>Neural Networks updated (can be used with the annotation tool)</li> <li>OMoCap data: <ul> <li>Annotation errors corrected</li> <li>Annotations reformatted, fitting the SARA annotation tool</li> <li>“additional annotated data” extended</li> <li>“Markers_Exports” added</li> </ul> </li> <li>IMU data (MbientLab and MotionMiners Sensors) <ul> <li>Annotation errors corrected</li> </ul> </li> <li>README file (protocol) updated and extended</li> </ul>   If you use this dataset for research, please cite the following paper: “LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes”, Sensors 2020, DOI: <a href="https://doi.org/10.3390/s20154083">10.3390/s20154083</a>. If you use the Mbientlab Networks, please cite the following paper: “From Human Pose to On-Body Devices for Human-Activity Recognition”, 25th International Conference on Pattern Recognition (ICPR), 2021, DOI: <a href="https://doi.org/10.1109/ICPR48806.2021.9412283">10.1109/ICPR48806.2021.9412283</a>. For any questions about the dataset, please contact Friedrich Niemann at friedrich.niemann@tu-dortmund.de.

Dataset for: Laser absorption spectroscopy measurements of different pulmonary oxygen gas concentrations in transmittance and remittance geometry – phantom study

Significance GASMAS technique has the potential for continuous, clinical monitoring of pre-term infant lung function, removing the need to X-ray diagnosis and reliance on indirect and relatively slow measurement of blood oxygenation. Aim To determine optimal source-detector configuration for reliable path lengths calculation and to estimate the oxygen gas concentration inside the lung cavities filled with humidified gas with four different oxygen gas concentrations ranging between 21% and 100%. Approach Anthropomorphic optical phantoms of neonatal thorax with two different geometries were used to acquire Gas in Scattering Media Absorption Spectroscopy (GASMAS) signals, for 30 source-detector configurations in transmittance and remittance geometry of phantoms in two sizes. Results The results show that an internal light administration is more likely to provide a high GASMAS signal-to-noise ratio (SNR). In general, better SNRs were obtained with the smaller set of phantoms. The values of path length and O2 concentrations calculated with signals from the phantoms with optical properties at 820 nm, exhibit higher variations than signals from the phantoms with optical properties at 764 nm. Conclusion The study shows that by moving the source and detector over the thorax, most of the lung volumes can potentially be assessed using GASMAS technique.

(EMPIR 19ENG06 HEFMAG) Data sets of measurements of magnetic loss and complex permeability on amorphous and nanocrystalline samples up to the MHz range

We measured the magnetic losses and the complex permeability of amorphous and nanocrystalline ribbons from DC to 1 GHz by combined application of fluxmetric and transmission line methods. Two transverse field annealed Co-based amorphous alloys, ~13 µm and ~25 µm tick and two nanocrystalline Finemet type alloys, ~13 µm and ~20 µm tick, endowed with defined transverse magnetic anisotropy, were characterized.

Metadata with submitted Emission Control Science and Technology Journal manuscript Traceable uncertainty of exhaust flow meters embedded in portable emission measurement systems

Metadata with submitted Emission Control Science and Technology Journal Traceable uncertainty of exhaust flow meters embedded in portable emission measurement systems Link to article: https://link.springer.com/article/10.1007/s40825-025-00260-z

opencc-zeroSep 2023View details →

dryad40/100

Data from: A user-friendly guide to using distance measures to compare time series in ecology

Time series are a critical component of ecological analysis, used to track changes in biotic and abiotic variables. Information can be extracted from the properties of time series for tasks such as classification (e.g. assigning species to individual bird calls); clustering (e.g. clustering similar responses in population dynamics to abrupt changes in the environment or management interventions); prediction (e.g. accuracy of model predictions to original time series data); and anomaly detection (e.g. detecting possible catastrophic events from population time series). These common tasks in ecological research rely on the notion of (dis-) similarity, which can be determined using distance measures. A plethora of distance measures have been described, predominantly in the computer and information sciences, but many have not been introduced to ecologists. Furthermore, little is known about how to select appropriate distance measures for time-series-related tasks. Therefore, many potential applications remain unexplored. Here we describe 16 properties of distance measures that are likely to be of importance to a variety of ecological questions involving time series. We then test 42 distance measures for each property and use the results to develop an objective method to select appropriate distance measures for any task and ecological dataset. We demonstrate our selection method by applying it to a set of real-world data on breeding bird populations in the UK and discuss other potential applications for distance measures, along with associated technical issues common in ecology. Our real-world population trends exhibit a common challenge for time series comparisons: a high level of stochasticity. We demonstrate two different ways of overcoming this challenge, first by selecting distance measures with properties that make them well-suited to comparing noisy time series, and second by applying a smoothing algorithm before selecting appropriate distance measures. In both cases, the distance measures chosen through our selection method are not only fit-for-purpose but are consistent in their rankings of the population trends. The results of our study should lead to an improved understanding of, and greater scope for, the use of distance measures for comparing ecological time series, and help us answer new ecological questions.

Measurements obtained for USRP B210 used for Angle of Arrival estimation

Results both for estimation of calibration phase (difference of phase between two RX antennas) and AoA error. attached python coeds for reading the samples. The whole setup described in <a href="https://doi.org/10.1007/s11276-022-03010-z">https://doi.org/10.1007/s11276-022-03010-z</a> The code for USRP AoA estimation available at https://github.com/MarcinWachowiak/gr-aoa.git

Measurements and simulations of the nightglow continuum at Cerro Paranal

The provided ASCII files constitute data sets that are related to the paper "Structure, variability, and origin of the low-latitude nightglow continuum between 300 and 1,800 nm: evidence for HO2 emission in the near-infrared", which has been published in Atmospheric Chemistry and Physics, Vol. 24, 2024 (https://doi.org/10.5194/acp-24-1143-2024).

Data from: Alternative measures of trait-niche relationships: a test on dispersal traits in saproxylic beetles (Ecology and Evolution)

Data from: Alternative measures of trait-niche relationships: a test on dispersal traits in saproxylic beetles (Ecology and Evolution) DATA DOI: https://doi.org/10.5281/zenodo.8322080 Associated article DOI: https://doi.org/10.1002/ece3.10588 Ryan C. Burner, Jorg Stephan, Juha Siitonen, Tord Snall, et al. 2023 ryan.c.burner@gmail.com This data release contains data files needed to run the Hmsc models described in the associated publication. It is a subset of the complete beetle capture and environmental covariate dataset maintained by Juha Siitonen (see associated manuscript for references to prior publications). It contains the following four files: 1) Species_detections.csv This site_year x species table has detection/non-detection (1/0) values for each species at each site_year. Beetles were trapped at about 142 sites in Finland forests. Includes only beetle species (n = 212) which are considered saproxylic and which were detected at >=5 sites in the dataset, and for which trait information was available. Species names are as originally identified in the source dataset (see early publications by Juha Siitonen). Row names ('Row_ID'), which consist of [site]_[year], correspond to 'Row_ID' in the 'Site_covariates.csv' file. Species (column) names correspond to species row naes in 'Species_traits.csv' 2) Site_covariates.csv This table has one row for each 'Row_ID' (n = 142) corresponding to rows in 'Species_data.csv'. Covariate columns have been scaled and centered for modeling. Columns are as follows: rowID - [site]_[year] of sampling Year - year of sampling Site - site name/number climID - unique ID for each grid cell from which climate data were extracted lat_WGS84 - latitude (WGS84) lon_WGS84 - longitude (WGS84) VD10 - scaled and centered total pooled volume of local standing and fallen dead trees (originally in m3/ha, before scaling) with a minimum diameter of 10 cm, estimated using transects agedomin - scaled and centered mean age of the five oldest trees in the stand OldFor_1km - scaled and centered volume of living wood in those forests older than 100 years within a one km radius around each site MeanTemp - scaled and centered mean temperature during the trapping period, from mean of all ERA5 hourly estimates of 2m temperature (see manuscript for details) TotalPrecip - scaled and centered total precipitation during the trapping period, from ERA5 summed across all hourly estimates of total precipitation (see manuscript for details) globRad_WHm2 - scaled and centered total solar radiation during the trapping period, summed across all daily values, based on site slope and aspect, calculated using GIS (see manuscript for details). Units were Wh/m2 prior to scaling and centering. log_Nr_traps - scaled and centered log-transformed number of traps used at each capture site  3) Species_traits.csv Trait data, based on trait values in Hagge et al. (2021 - see manuscript for full reference), for beetle species included in model (see species data information, above). In some cases traits are from synonyms used in Hagge that differ from taxonomy of this dataset. Traits have been scaled and centered. Row names are species names that match columns in 'Species_detections.csv'. Columns as follows: wing_length - scaled and centered (log(wing length divided by body length)) wing_load - scaled and centered (log(mass / wing area / body length)) wing_aspect - scaled and centered (log(wing aspect ratio) 4) Phylotree.csv A phylogenetic tree for the species in this dataset, written in the Newick (also known as New Hampshire) format. The tree is based on the species-level insect tree in Chesters et al. (2017) (see manuscript for full citation) but has missing species added randomly to the correct genus (when present) or family or (occassionally) order.

Code and dataset from the winter chase measurements

Code and dataset to the article Leinonen, V., Olin, M., Martikainen, S., Karjalainen, P., and Mikkonen, S.: Challenges and solutions in determining dilution ratios and emission factors from chase measurements of passenger vehicles, 2023. See Info.txt for info.

Dataset - Speeding up high-throughput characterization of materials libraries by active learning: autonomous electrical resistance measurements

With the trend towards multinary materials and the associated increase in measurement time, there is a clear need for increasing the efficiency of measurement procedures. In systems requiring long materials characterization times, the implementation of active learning can help decreasing the measurement duration significantly. This dataset is part of the publication in Digital Discovery under the same title and holds the algorithm as well as the data used to test its performance. The algorithm leverages an active learning approach with a Gaussian process model capable of selecting the next measurement area of a library of materials based on the highest uncertainty. Ten materials libraries were manufactured by magnetron sputtering, the composition was measured with EDX and the electrical resistance was measured using the described test stand. The code can also be found on <a href="https://gitlab.ruhr-uni-bochum.de/fthelen/auto-resist-meas">Gitlab</a>.

Dataset for the collected responses for the items measuring constructs affecting eHS non-acceptance behavior in Nigeria

This dataset is a collection of responses from the questionnaire distributed to study the e-health service non-acceptance behavior prominent in Nigeria. A total of 543 valid responses were collected. This research used an integration model based on TPB and SOR theory The dataset were analysed using PLS-SEM. Refer to the article for the results of this study. Note: CO = Communication overload; CHO = Choice overload; PR = Perceived oisk; HL = Health literacy; NA = Negative attitude; SN = Subjective norms; PBC = Perceived behavioral control; INTU = Intention not to use eHS; NAB = Non-acceptance behavior

opencc-by-4.0Mar 2023View details →

Dataset For "Nyiragongo crater collapses measured by multi-sensor SAR amplitude time series"

This archive contains the input ant results files used with PickCraterSAR for publication "Nyiragongo crater collapses measured by multi-sensor SAR amplitude time series" submitted to JGR-SE. It also contains crops of each amplitude images used in this study in ENVI format with corresponding headers. At least, it contains the ash index values derives from SEVIRI data analysis.

H2020 Platone German Demonstrator Use Case 1, 2, 3 and 4 Measurement Data

This dataset belongs to the German demonstrator of the H2020 Platone project (WP5). This dataset contains measurement data and processed data relevant for the evaluation of UseCases (UCs) applied in the field test side. Background - Field Test Setup The field test setup consists of a Low Voltage (LV) community with 450 kW installed generation capacity. The power exchange between the LV grid and Medium Voltage (MV) grid takes place along a single Point of Common Coupling (PCC). i.e., a secondary substation that includes a transformer with sensors on the LV busbar to measure the net power exchange. The community consists of 89 households, 450kW of installed PV generation capacity, a Community Battery Energy Storage (CBES) connected to the LV busbar with 300 kW and 850 kWh capacity.  Description of data set: p_tei - arithmetic mean of measured power exchange at PCC (Total residual power exchange Export/Import) measured in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count) p_tcb - arithmetic mean of measured charging/discharging power of CBES in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count) p_tcb_set – triggered charging/discharging power of CBES p_tei_c - Computed power exchange at PPC. That value indicates the value p_tei if no UC would have been applied (baseline). e_im –cumulated measured energy import (from MV grid into LV grid) e_ex - cumulated measured energy export (from LV grid into MV grid) soc – State Of Charge of CBES soc_max – maximum permissible SOC of CBES soe - State Of Energy of CBES soc_min – minimum permissible SOC of CBES id – ID of UC that is active at point of time setpoint - Charging/discharging power for CBES triggered by EMS (ALF-C) during active an UC subtype - 0 - Rule-Based Operation Mode with 15-minutes control cycles of battery (CBES in the field) ;1 - Day-ahead forecast-based control; 2.0 - Schedule-based operation mode with optimization applied to a day-ahead forecast (optimization target: minimization of power exchanges at MV/LV PCC within 24h period ; 21 - Schedule-based operation mode with optimization applied to a day-ahead forecast (optimization target: minimization of power exchanges at MV/LV PCC and achieving a requested State of Charge (of CBES) at the end of UC_End; type – Triggered Type of UC (1 - "Virtual Islanding of LV community" (UC 1); 2 - "Coordination of Flex Request" (UC 2); 3 - "Energy Import in Bulk" (UC 3); 4 - "Bulk-based Energy Export" (UC 4) bulk –  (yes/no) – indicates whether bulk energy import or export is active. Only relevant for UC 3 and 4. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300.