Find research datasets worth reusing

opencc-by-4.0May 2023View details →

Language Recognition for SSB modulated HF Radio Signals of Short Duration - Example Files

These files are example files of the dataset used in our work "Language Recognition for SSB modulated HF Radio Signals of Short Duration". Two files are 10 second HF radio segments from Russian amateur radio communications. The other six files are a single recording from CommonLanguage (https://huggingface.co/datasets/common_language) and five modified versions of this file, created by applying the proposed HF radio simulation approach. Additional details can be found in the Paper.

opencc-by-4.0Aug 2023View details →

Dataset on the Human Body as a Signal Propagation Medium

Overview: This is a large-scale dataset with impedance and signal loss data recorded on volunteer test subjects using low-voltage alternate current sine-shaped signals. The signal frequencies are from 50 kHz to 20 MHz. Applications: The intention of this dataset is to allow to investigate the human body as a signal propagation medium, and capture information related to how the properties of the human body (age, sex, composition etc.), the measurement locations, and the signal frequencies impact the signal loss over the human body. Overview statistics: <ul> <li>Number of subjects: 30</li> <li>Number of transmitter locations: 6</li> <li>Number of receiver locations: 6</li> <li>Number of measurement frequencies: 19</li> <li>Input voltage: 1 V</li> <li>Load resistance: 50 ohm and 1 megaohm</li> </ul> Measurement group statistics: <ul> <li>Height: 174.10 (7.15)</li> <li>Weight: 72.85 (16.26)</li> <li>BMI: 23.94 (4.70)</li> <li>Body fat %: 21.53 (7.55)</li> <li>Age group: 29.00 (11.25)</li> <li>Male/female ratio: 50%</li> </ul> Included files: <ul> <li>experiment_protocol_description.docx - protocol used in the experiments</li> <li>electrode_placement_schematic.png - schematic of placement locations</li> <li>electrode_placement_photo.jpg - visualization on the experiment, on a volunteer subject</li> <li>RawData - the full measurement results and experiment info sheets</li> <li>all_measurements.csv - the most important results extracted to .csv</li> <li>all_measurements_filtered.csv - same, but after z-score filtering</li> <li>all_measurements_by_freq.csv - the most important results extracted to .csv, single frequency per row</li> <li>all_measurements_by_freq_filtered.csv - same, but after z-score filtering</li> <li>summary_of_subjects.csv - key statistics on the subjects from the experiment info sheets</li> <li>process_json_files.py - script that creates .csv from the raw data</li> <li>filter_results.py - outlier removal based on z-score</li> <li>plot_sample_curves.py - visualization of a randomly selected measurement result subset</li> <li>plot_measurement_group.py - visualization of the measurement group</li> </ul> CSV file columns: <ul> <li>subject_id - participant's random unique ID</li> <li>experiment_id - measurement session's number for the participant</li> <li>height - participant's height, cm</li> <li>weight - participant's weight, kg</li> <li>BMI - body mass index, computed from the valued above</li> <li>body_fat_% - body fat composition, as measured by bioimpedance scales</li> <li>age_group - age rounded to 10 years, e.g. 20, 30, 40 etc.</li> <li>male - 1 if male, 0 if female</li> <li>tx_point - transmitter point number</li> <li>rx_point - receiver point number</li> <li>distance - distance, in relative units, between the tx and rx points. Not scaled in terms of participant's height and limb lengths!</li> <li>tx_point_fat_level - transmitter point location's average fat content metric. Not scaled for each participant individually.</li> <li>rx_point_fat_level - receiver point location's average fat content metric. Not scaled for each participant individually.</li> <li>total_fat_level - sum of rx and tx fat levels</li> <li>bias - constant term to simplify data analytics, always equal to 1.0</li> </ul> CSV file columns, frequency-specific: <ul> <li>tx_abs_Z_... - transmitter-side impedance, as computed by the `process_json_files.py` script from the voltage drop</li> <li>rx_gain_50_f_... - experimentally measured gain on the receiver, in dB, using 50 ohm load impedance</li> <li>rx_gain_1M_f_... - experimentally measured gain on the receiver, in dB, using 1 megaohm load impedance</li> </ul> Acknowledgments: The dataset collection was funded by the Latvian Council of Science, project “Body-Coupled Communication for Body Area Networks”, project No. lzp-2020/1-0358. References: For a more detailed information, see this article:  J. Ormanis, V. Medvedevs, A. Sevcenko, V. Aristovs, V. Abolins, and A. Elsts. Dataset on the Human Body as a Signal Propagation Medium for Body Coupled Communication. Submitted to Elsevier Data in Brief, 2023. Contact information: info@edi.lv

opencc-by-3.0-usSep 2023View details →

Data for 2D Signal Estimation for Sparse Distributed Target Photon Counting Data

Data used in publication of 2D Signal Estimation for Sparse Distributed Target Photon Counting Data Data provided consists of: raw and PTV processed MicroPulse DIAL (MPD) data (10.26023/MX0D-Z722-M406) simulated photon counting data and the processed results

opencc-by-4.0Sep 2023View details →

Monitoring valve activity in M.edulis and M.galloprovincialis: Valve signal processing

This repository provides access to the metadata and scripts used to monitor valve gaping activity of bivalves using valvometry (Valve-Trek ; Technosmart Europe srl, www.technosmart.eu). The dataset contains valve activity records in csv format per individual sampled from February to May 2023 at Agon-Coutainville (Normandy, France), as well as r scripts for processing, formatting and analyzing valve gaping data.

opencc-by-4.0Oct 2023View details →

Source data for "Feed-forward metabotropic signaling by Cav1 Ca2+ channels supports pacemaking in pedunculopontine cholinergic neurons"

Fig.1A_ChAT.tifConfocal image (green channel, anti-ChAT staining) for Fig.1A Fig.1A_tdTomato.tif Confocal image (red channel, tdTomato) for Fig.1A Fig.1B_ChAT.tifConfocal image (green channel, anti-ChAT staining) for Fig.1B Fig.1B_tdTomato.tifConfocal image (red channel, tdTomato) for Fig.1B Fig.1C_DIC.pngDifferential interference contrast micrograph for Fig.1C left Fig.1C_Fluo.pngEpifluorescent illumination micrograph for Fig. 1C right Fig.1DEH.xlsxNumerical data for the charts in Fig. 1D, Fig.1E, Fig.1H Fig.1F.tifMAX projection of z-stack of 2PLSM images (red channel, Alexa 594) used to generate Fig.1F  Fig.1F_inset.tif2PLSM image (green channel, Fura-2) for the right inset of Fig.1F Fig.2A_inset.tifConfocal image (green channel, GFP) for the higher magnification inset of Fig.2A Fig.2A.tifConfocal image (green channel, GFP) for Fig.2A Fig.2B_bottom.tifConfocal image (green channel, GFP) for Fig.2B (bottom and overlay panels) Fig.2B_top.tifConfocal image (red channel, td Tomato) for Fig.2B (top and overlay panels) Fig.2CE.xlsxNumerical data for the charts in Fig. 2C, Fig. 2E Fig.3B.tifConfocal image (green channel, MitoGCaMP6) for Fig.3B and overlay in Fig.3D Fig.3C.tifConfocal image (red channel, tdTomato) for Fig.3C and overlay in Fig.3D Fig.3E.tif2PLSM image (green channel, MitoGCaMP6) for Fig.3E Fig.3GIJ.xlsxNumerical data for the charts in Fig. 3G, Fig. 3I, Fig.3J Fig.4B.tif2PLSM image (green channel, MitoGCaMP6) for Fig.4B Fig.4DFG.xlsxNumerical data for the charts in Fig.4D, Fig.4F, Fig.4G Fig.5A.tifConfocal image (green channel, PercevalHR) for Fig.5A and overlay in Fig.5C Fig.5B.tifConfocal image (red channel, tdTomato) for Fig.5B and overlay in Fig.5C Fig.5D.tif2PLSM image (green channel, PercevalHR) for Fig.5D Fig.5GHJ.xlsxNumerical data for the charts in Fig.5G, Fig.5H, Fig.5J Fig.6BCD.xlsxNumerical data for the charts in Fig.6b, Fig.6C, Fig.6D Fig.7A.tifConfocal image (green channel, mito-roGFP) for Fig.7A and overlay in Fig.7C Fig.7B.tifConfocal image (red channel, tdTomato) for Fig.7B and overlay in Fig.7C Fig.7D.tif2PLSM image (green channel, mito-roGFP) for Fig.7D Fig.7F.xlsxNumerical data for the charts in Fig.7F

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Stachewicz et al. 2021: Trait correlation, phylogenetic signal in Carabidae morphology (repackaging of occurrences published by the NEON Biorepository Data Portal)

Stachewicz JD, Fountain-Jones NM, Koontz A, Woolf H, Pearse WD, Gallinat AS. 2021. Strong trait correlation and phylogenetic signal in North American ground beetle (Carabidae) morphology bioRxiv 02.12.431029; doi: https://doi.org/10.1101/2021.02.12.431029 Many NEON samples and specimens used in this work resulted from NEON prototype data and will not be archived in the Biorepository. See the appendices in the above linked article for a full list of NEON samples and specimens and their associated collection data. Additionally, see appendices of above linked article for specimen-level morphological trait measurements and genetic sequence data.

openCC0Feb 2023View details →

opencc-by-4.0Feb 2020View details →

Data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks"

This upload contains the data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks", (D.O.I: <a href="https://doi.org/10.3390/jsan9010012">10.3390/jsan9010012</a>) published in the the special issue on "Localization in Wireless Sensor Networks" of the <a href="https://www.mdpi.com/journal/jsan">Journal of Sensor and Actuator Networks</a> (ISSN 2224-2708). The data and code included allows to replicate the results of the article.

opencc-by-4.0Mar 2020View details →

A fresh look at the gravitational-wave signal from cosmological phase transitions

Supplemental material for the paper of the same name, arXiv:1909.11356 [hep-ph], consisting of (1) the set of model benchmark points used in our analysis and (2) our Jupyter notebook for generating the peak-integrated sensitivity plots shown in the paper.

opencc-by-4.0May 2020View details →

Constraining properties of the next nearby core-collapse supernova with multi-messenger signals: gravitational wave frequency fits

1D FLASH simulations with STIR, for alpha_lambda = 1.23, 1.25, and 1.27.  Run with SFHo EOS, M1 with 12 energy groups. For more information on these simulations, see Warren, Couch, O'Connor, & Morozova (arXiv:1912.03328) and Couch, Warren, & O'Connor (2020). Includes fit to the gravitational wave peak frequency versus time post-bounce, for a functional fit of the form f = A*sqrt(t) + B*t + C, where the frequency f is in Hz and the time t is in seconds.  The columns are: progenitor mass [M_sun], fit coefficient A, fit coefficient B, fit coefficient C, and the R^2 of the fit.

opencc-by-4.0May 2020View details →

A VTA to basal amygdala dopamine projection contributes to signal salient somatosensory events during fear learning

This dataset represents the raw data that gave rise to the study by Tang et al., J. Neuroscience 2020 (DOI: 10.1523/JNEUROSCI.1796-19.2020). Please refer to the original publication regarding experimental design and methodological details of data acquisition and analysis.  Below we supply information on the provided metadata files (which, in turn, refer to individual raw data files), and essential details on the specific data formats. <ol> <li>raw data is organized in the datasets related to the Figs. 1H-L, 1M, 3B-C, 2, 4G-H, 5D-E, 5H-I of the paper (Tang et al., 2020);</li> <li>the metadata for each individual dataset, which lists individual data filenames from the respective dataset, are stored in separate “.csv” files, one per each dataset. Field separator: comma;</li> <li>individual metadata files for each dataset are described in the master metadata file “metadata_master.csv”. Field separator: comma;</li> <li>widefield fluorescent images of single coronal slices containing VTA (Fig. 1H-M) were converted from the proprietary format of Olympus slide scanning microscope into composite TIFF format readable by FIJI/ImageJ (<a href="https://fiji.sc/">https://fiji.sc/</a> or <a href="https://imagej.net/Fiji/Downloads">https://imagej.net/Fiji/Downloads</a>). Image stacks covering the injection area of CTB into BA are provided in single multi-plane TIFF files, one per animal.  Unless specified in the metadata file, the information on pixel resolution is embedded inside the individual image files as TIFF metadata. Attribution of fluorescent probes to the color channels is given in the corresponding metadata files; AP positions refer to Franklin KB, Paxinos G (2016) The mouse brain in stereotaxic coordinates, Ed 4. San Diego: Elsevier/Academic;</li> <li>confocal fluorescent image stacks acquired from single coronal slices containing VTA (Fig. 3B-C) are provided in the original format “.lsm” written by a confocal software Zen (Carl Zeiss). Besides the original Zen software, this format can be readily imported into FIJI/ImageJ using built-in converters “LSM…” or “Bio-Formats”. All the metadata containing imaging parameters is embedded in this format and can be accessed from FIJI/ImageJ after importing the stack. Attribution of fluorescent probes to the color channels is given in the corresponding “.csv” metadata file; AP positions refer to Franklin and Paxinos (2016);</li> <li>video recordings of animal behavior are provided as “.wmv” files, unmodified from the original version created by the acquisition software VideoFreeze (MedAssociates Inc). Video stream parameters: wmv3 codec, color space yuv420p, 320x240 pixels, bitrate 300 kb/s, 30 fps;</li> <li>timing patterns of the sound (CS) and of the footshock (US) applications during each day of fear conditioning protocol are provided in the respective “.csv” files: “day1_CS_timing.csv”, “day2_CS_timing.csv”, “day2_US_timing.csv”, “day3_CS_timing.csv”. The timestamps in these files are expressed in seconds relative to the start of the VideoFreeze video recordings (see p. 4 above). These patterns are in common for all the video recordings done on respective training days in every data subset (Figs. 2, 4-5);</li> <li>extracellular optrode recording data (Fig. 2) were converted from the original proprietary “.mcd” format of the MC_Rack software (Multichannel Systems) into the open HDF5 format “.h5” using the Multi Channel DataManager software (Multichannel Systems). We provide both the continuous recording data acquired during behavior sessions on days 1-3 of the fear learning protocol, as well as recordings of light-evoked spiking acquired during the opto-tagging sessions on each experimental day. In the latter, each of 8-10 consequently recorded data files contains individual triggered sweeps (from -50 to +50 ms), each centered around a single laser pulse (t=0 ms);</li> <li>the raw unfiltered electrode data is stored as a 32-bit integer matrix 16xN (16 - number of electrodes, N - number of sampling points @ 40 kHz) in the container Data->Recording_0->AnalogStream->Stream_1->ChannelData of the HDF5 files. Conversion factor to the units of volts for the raw values is 1.25e-6. Correspondence of the rows of the data matrix to the electrodes “E1”-“E16” is indexed by the string array Data->Recording_0->AnalogStream->Stream_1->I_Label. The electrodes were physically arranged into four tetrodes in following groups: E1-E4, E5-E8, E9-E12, E13-E16;</li> <li>timestamps for the continuous recordings, or for each of the triggered sweeps in case of opto-tagging, are stored in the 2D floating point array Data->Recording_0->AnalogStream->Stream_1->ChannelDataTimeStamps; the timestamp values are in microseconds;</li> <li>the timing of CS and US stimuli produced by the VideoFreeze software (see p. 5 above) was sampled as input digital triggers by the amplifier for extracellular recordings for precise synchronization between the optrode- and video recordings. These signals are stored as single bit changes in the 32-bits integer N-samples array Data->Recording_0->AnalogStream->Stream_0->ChannelData of the corresponding HDF5 files.</li> </ol>

opencc-by-4.0May 2020View details →

Data and Code for: Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals

This repository contains the datasets and scripts used to obtain the figures of the paper "Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals". The repository is organized as follows: - Part I) Monte Carlo simulation codes - Part II) Monte Carlo simulations using the parameter configuration "Param. 1" of Shi et al. (1999) - Part III) Monte Carlo simulation using the parameter configuration "Param. 1" of Shi et al. (1999) and a limited ion composition search space - Part IV) Monte Carlo simulations using the parameter configuration "Param. 2" of Wang et al. (2012) - Part V) Monte Carlo simulation using the parameter configuration "Param. 2" of Wang et al. (2012) and a limited ion composition search space - Part VI) Codes to generate all figures of the manuscript All datasets and scripts were generated and tested using Matlab 2017. Simulations have been executed in parallel on a SLURM cluster, compilation and running scripts are provided.

Figure 3 in Review of the Australian Signal Flies of the Genus Microepicausta (Diptera: Platystomatidae)

Figure 3. Microepicausta gracilis Hendel, Bronte, NSW. Details of male postabdomen, from retouched photograph. Scale = 0.5 mm. c, cercus; e, epandrium; g, glans; is, inner surstylus; st, stipe; tf, terminal filament.

Figures 10–12 in Review of the Australian Signal Flies of the Genus Microepicausta (Diptera: Platystomatidae)

Figures 10–12. Microepicausta wirthi sp. nov., Nullica Beach, NSW. Details of male postabdomen. (10) Right surstyli and fused cerci. Scale = 0.1 mm. (11) Aedeagus (retouched) and associated structures. Scale = 0.2 mm. (12) Left wing.

Figures 1, 2 in Review of the Australian Signal Flies of the Genus Microepicausta (Diptera: Platystomatidae)

Figures 1, 2. Microepicausta gracilis Hendel, male, Bronte, NSW. (1) Head and part of thorax. (2) Right wing.

Figures 6, 7 in Review of the Australian Signal Flies of the Genus Microepicausta (Diptera: Platystomatidae)

Figures 6, 7. Microepicausta fenestra sp. nov., Musgrave, Qld. Details of male postabdomen. (6) Right cercus and surstyli. Scale = 0.1 mm. (7) Aedeagus and associated structures. Scale = 0.3 mm. aa, aedeagal apodeme; as, aedeagal sheath; b, bulb of aedeagus; g, glans; mw, membranous wing of stipe; pa, process of aedeagal apodeme (paired); pg, preglans; st, stipe; tf, terminal filament; tp, terminal process of bulb.

Figures 4, 5 in Review of the Australian Signal Flies of the Genus Microepicausta (Diptera: Platystomatidae)

Figures 4, 5. Microepicausta fenestra sp. nov., holotype male. (4) head and part of thorax. (5) right wing.

Figure 13 in Review of the Australian Signal Flies of the Genus Microepicausta (Diptera: Platystomatidae)

Figure 13. This depiction of Dee Why, New South Wales (near Sydney), painted by Jules Pierre (Jan) De Leener (1873–1944)

Figures 8, 9 in Review of the Australian Signal Flies of the Genus Microepicausta (Diptera: Platystomatidae)

Figures 8, 9. Microepicausta wirthi sp. nov., Nullica Beach, NSW. (8) Holotype male. (9) Female, head and thorax.

opencc-by-4.0Jun 2020View details →

Dataset: Accuracy of Motor Error Predictions for Different Sensory Signals

Supplementary Data for Accuracy of Motor Error Predictions for Different Sensory Signals article Dataset associated with the following publication: Joch, M., Hegele, M., Maurer, H., Müller, H., & Maurer, L. K. (2018). Accuracy of Motor Error Predictions for Different Sensory Signals. Frontiers in Psychology, 9 (August), 1–13. https://doi.org/10.3389/fpsyg.2018.01376