Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
8,460
datasets available to search
ShareScore release 0.9.0
Dataset results
8,460 results for “MONITORING”
Effects-based Monitoring of Contaminants of Emerging Concern in Great Lakes Tributaries: Calculating the Risk of Fish Developing Liver Toxicity and Cancer
GEO Series GSE146903. Homo sapiens. 71 samples. Type: Expression profiling by high throughput sequencing.
Yeast Hsp90-dependent chromatin changes as monitored using DNase-Seq
GEO Series GSE88875. Saccharomyces cerevisiae. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Long-term in vivo monitoring of transplanted mesenchymal stromal cells in colitis mice with magnetic particle imaging
GEO Series GSE289684. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Identification of mRNAs as potential blood based biomarekrs for diagnosis and therapeutic monitoring of active tuberculosis
GEO Series GSE279063. Homo sapiens. 65 samples. Type: Expression profiling by high throughput sequencing.
Functional dissection of hematopoietic stem cell population by a stemness-monitoring system with NS-GFP transgene
GEO Series GSE98500. Mus musculus. 12 samples. Type: Expression profiling by array.
Single cell RNA sequencing monitoring the development of allogeneic HSC-derived CAR-engineered NKT cells
GEO Series GSE241996. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
A chemical genetic method for monitoring genome-wide dynamics of O-GlcNAc turnover on chromatin-associated proteins.
GEO Series GSE124785. Drosophila melanogaster. 38 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Exosomal miR-432-5p, miR-4433b-5p and miR-599 as Biomarkers for Monitoring the Severity of Anti-N-methyl-D-aspartate Receptor Encephalitis
GEO Series GSE287388. Homo sapiens. 17 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Monitoring Burma mangrove transcriptome changes in response to salt and osmotic stress.
GEO Series GSE7228. Bruguiera gymnorhiza. 19 samples. Type: Expression profiling by array.
Monitor the dynamic changes of GBM cell lines under TTFields treatment
GEO Series GSE270513. Homo sapiens. 52 samples. Type: Expression profiling by high throughput sequencing.
Figure 1 in Terrestrial isopods as bioindicators for environmental monitoring in olive groves and natural ecosystems
Figure 1. Study area (western magnesia prefecture, central Greece).
Monitoring data_2021 (1)
<p>Monitoring data of the soil suction and volumetric water content measured at the standard vegetation area (A) and deep-rooting vegetation area (P), together with atmospheric data and rainfalls, for 2021.</p>
BAF: an audio fingerprinting dataset for broadcast monitoring
<p><strong>Overview</strong></p> <p>Broadcast Audio Fingerprinting dataset is an open, available upon request, annotated dataset for the task of music monitoring in broadcast. It contains 2,000 tracks from Epidemic Sound's private catalogue as reference tracks that represent 74 hours. As queries, it contains over 57 hours of TV broadcast audio from 23 countries and 203 channels distributed with 3,425 one-min audio excerpts.</p> <p>It has been annotated by six annotators in total and each query has been cross-annotated by three of them obtaining high inter-annotator agreement percentages, which validates the annotation methodology and ensures the reliability of the annotations.</p> <p><strong>Purpose of the dataset</strong></p> <p>This dataset aims to become the standard dataset to evaluate Audio Fingerprinting algorithms since it’s built on real data, without the use of any data-augmentation techniques. It is also the first dataset to address background music fingerprinting, which is a real problem in royalties distribution.</p> <p><strong>Dataset use</strong></p> <p>This dataset is available for conducting non-commercial research related to audio analysis. It shall not be used for music generation or music synthesis.</p> <p><strong>About the data</strong></p> <p>All audio files are monophonic, 8kHz, 128kb/s, pcm_s16le encoded in .wav. Annotations mark which tracks sound (either in foreground or background) in each query (if any) and also the specific times where it starts and ends sound in the query.</p> <p><em>Note that there are 88 queries that do not have any matches.</em></p> <p>For more information check the dedicated Github repository: <a href="https://github.com/guillemcortes/baf-dataset">https://github.com/guillemcortes/baf-dataset</a> and the dataset datasheet included in the files.</p> <p> </p> <p><strong>Dataset contents</strong></p> <p>The dataset is structured following this schema</p> <pre><code>baf-dataset/ ├── baf_datasheet.pdf ├── annotations.csv ├── changelog.md ├── cross_annotations.csv ├── queries_info.csv ├── queries │ ├── query_0001.wav │ ├── query_0002.wav │ ├── … │ └── query_3425.wav ├── queries_info.csv └── references ├── ref_0001.wav ├── ref_0002.wav ├── … └── ref_2000.wav</code></pre> <p>There are two folders named <strong>queries</strong> and <strong>references</strong> containing the <em>wav</em> files of TV broadcast recordings and the reference tracks, respectively.</p> <p><strong>annotations.csv</strong> file contains the annotations made by the 6 annotators, giving the following information:</p> <table align="center" summary="annotations.csv content summary"> <caption>annotations.csv content summary</caption> <thead> <tr> <th scope="col">query</th> <th scope="col">reference</th> <th scope="col">query_start</th> <th scope="col">query_end</th> <th scope="col">annotator</th> </tr> </thead> <tbody> <tr> <td>query_0692.wav</td> <td>ref_1235.wav</td> <td>0.0</td> <td>59.904</td> <td>annotator_6</td> </tr> </tbody> </table> <p><strong>cross_annotations.csv </strong>contains the resulting annotations after merging the overlapping annotations in <strong>annotations.csv</strong> file. x_tag has three different values:</p> <ul> <li> <p>single: the segment has only been annotated by one annotator.</p> </li> <li> <p>majority: the segment has been annotated by two annotators.</p> </li> <li> <p>unanimity: the segment has been annotated by the three annotators.</p> </li> </ul> <table align="center" summary="cross_annotations.csv content summary"> <caption>cross_annotations.csv content summary</caption> <thead> <tr> <th scope="col">query</th> <th scope="col">reference</th> <th scope="col">query_Start</th> <th scope="col">query_end</th> <th scope="col">annotators</th> <th scope="col">x_tag</th> </tr> </thead> <tbody> <tr> <td>query_0693.wav</td> <td>ref_1834.wav</td> <td>37.53</td> <td>38.07</td> <td>['annotator_3']</td> <td>single</td> </tr> <tr> <td>query_0693.wav</td> <td>ref_1834.wav</td> <td>18.18</td> <td>37.48</td> <td>['annotator_3', 'annotator_5', 'annotator_3']</td> <td>unanimity</td> </tr> <tr> <td>query_0693.wav</td> <td>ref_1834.wav</td> <td>37.48</td> <td>37.53</td> <td>['annotator_5', 'annotator_3']</td> <td>majority</td> </tr> </tbody> </table> <p><strong>queries_info.csv </strong>contains information about the queries as a citation reference. It contains the country, the channel and the date where the broadcast happened.</p> <table align="center" summary="queries_info.csv content summary"> <caption>queries_info.csv content summary</caption> <thead> <tr> <th scope="col">filename</th> <th scope="col">country</th> <th scope="col">channel</th> <th scope="col">datetime</th> </tr> </thead> <tbody> <tr> <td>query_0001.wav</td> <td>Norway</td> <td>Discovery Channel</td> <td>2021-02-26 14:45:26</td> </tr> </tbody> </table> <p><strong>changelog.md </strong>contains a curated, chronologically ordered list of notable changes for each version of the dataset.</p> <p><strong>baf_datasheet.pdf</strong> contains standardized documentation for datasets</p> <p> </p> <p><strong>Ownership of the data</strong></p> <p>Next, we specify the ownership of all the data included in BAF: Broadcast Audio Fingerprinting dataset. For licensing information, please refer to the “License” section.</p> <p><strong>Reference tracks</strong></p> <p>The reference tracks are owned by Epidemic Sound AB, which has given a worldwide, revocable, non-exclusive, royalty-free licence to use and reproduce this data collection consisting of 2,000 low-quality monophonic 8kHz downsampled audio recordings.</p> <p><strong>Query tracks</strong></p> <p>The query tracks come from publicly available TV broadcast emissions so the ownership of each recording belongs to the channel that emitted the content. We publish them under the right of quotation provided by the Berne Convention.</p> <p><strong>Annotations</strong></p> <p>Guillem Cortès together with Alex Ciurana and Emilio Molina from BMAT Music Licensing S.L. have managed the annotation therefore the annotations belong to BMAT.</p> <p> </p> <p><strong>Accessing the dataset</strong></p> <p>The dataset is available upon request. Please include, in the justification field, your academic affiliation (if you have one) and a brief description of your research topics and why you would like to use this dataset. Bear in mind that this information is important for the evaluation of every access request.</p> <p> </p> <p><strong>License</strong></p> <p>This dataset is available for conducting non-commercial research related to audio analysis. <strong>It shall not be used for music generation or music synthesis</strong>. Given the different ownership of the elements of the dataset, the dataset is licensed under the <strong>following conditions</strong>:</p> <ol> <li> <p>User’s access request</p> </li> <li> <p>Research only, non-commercial purposes</p> </li> <li> <p>No adaptations nor derivative works</p> </li> <li> <p>Attribution to Epidemic Sound and the authors as it is indicated in the ”citation” section.</p> </li> </ol> <p><strong>Please include, in the justification field, your academic affiliation (if you have one) and a brief description of your research topics and why you would like to use this dataset.</strong></p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>With the support of Ministerio de Ciencia Innovación y universidades through Retos-Colaboración call, reference: RTC2019-007248-7, and also with the support of the Industrial Doctorates Plan of the Secretariat of Universities and Research of the Department of Business and Knowledge of the Generalitat de Catalunya. Reference: DI46-2020.</p>
Towards Using Virtual Acoustics for Evaluating Spatial Ecoacoustic Monitoring Technologies - Data
<h1>About</h1> <p>This database contains the raw data and certain outputs used for the project 'Towards Using Virtual Acoustics for Evaluating Spatial Ecoacoustic Monitoring Technologies'. In this work, we developed an ambisonic Virtual Sound Environment (VSE) for simulating real natural soundscapes to evaluate spatial PAM technologies in a more controlled and repeatable manner.</p> <p>We set three objectives to validate this approach:</p> <ul> <li>(O1) to determine whether the VSE could replicate natural soundscapes well enough to be a test environment;</li> <li>(O2) to pilot the VSE as a test environment for Passive Acoustic Monitoring (PAM) hardware; and, </li> <li>(O3) to pilot the VSE as a test platform for PAM software.</li> </ul> <p>To meet these objectives, we used a recently-developed, open source six-microphone field recorder to capture recordings of six field sites and their VSE-based simulations. Sites were based at the Imperial College Silwood Park Campus (Ascot, UK). For O1, we compared field and VSE recordings using a typical suite of ecoacoustic metrics. For O2, we used the VSE to explore how orientation impacts the performance of the six-microphone array. We extended the suite of metrics from O1 to compare VSE recordings from this array at various pitch angles: vertical (as in the field), 45° pitch, and horizontal. For O3, we investigate how BirdNET and HARKBird, software for classifying and localising avian calls, respectively, perform on bird calls added to the VSE-replicated soundscapes. We compare adding calls by encoding to the ambisonics domain and by playback from individual loudspeakers. </p> <p> </p> <h3><strong>The data is organised as follows: </strong></h3> <p><strong>'6mic Audio O1O2': </strong>contains the six-channel field and VSE-based recordings of all six sites. Recordings are approximately 10 minutes long. Note that there are three VSE-based recording per site, for each recording orientation used in the VSE (vertical, 45°, and horizontal). Several of our analyses used low-passed versions of these recordings (4 kHz cutoff and 12 dB roll-off); note that the recordings provided here are raw and therefore not low-passed.</p> <ul> <li>File names indicate whether the recording was performed in the field or VSE ('Field' vs 'ReRec') and the orientation of the 6mic array for the latter ('V', '45', or 'H').</li> <li>The final number in each filename indicates the Site the recording corresponds to (1-6).</li> </ul> <p><strong>'6mic Audio O3':</strong> contains the six-channel VSE recordings of five of the original sites with additional avian calls located at certain moments in space and time in each recording. 10 bird calls were added to the soundscapes, each one in its own soundscape recording (each of the five sites' soundscapes was therefore used twice). Four methods of adding the bird calls were trialled, hence there are 40 files in this folder – 10 for each of: ambisonic encoding, playback from individual loudspeakers, playback from individual loudspeakers with no reverb, and playback from individual loudspeakers with no reverb or background soundscape (i.e., 'soloed'). Our analyses focussed on comparing the first two of these methods. The filenames are structured as follows:</p> <ul> <li> "sXsYbirdNameEmbeddingMethod', where X corresponds to the site the background soundscape was recorded at,</li> <li>Y indicates the position of the sinusoidal sweep used to create simulated reverb of the bird call (1-4 for 0°, 90°, 180°, or 270° around the device used to capture the ambisonic soundscape recordings),</li> <li>'birdName' is the common name of the added species, and</li> <li>'EmbeddingMethod' is either: <ul> <li>'Ambi' (ambisonic encoding),</li> <li>'LSPK' (playback from an individual loudspeaker),</li> <li>'LSPK-NR' (playback from an individual loudspeaker with no reverb), or</li> <li>'Solo' (soloed playback from an individual loudspeaker).</li> </ul> </li> <li>Note that due to issues of spatial aliasing, we low-passed the 'Ambi' and 'LSPK' recordings in our analyses (as the two main embedding methods compared) using a 4 kHz cutoff and 12 dB roll-off. However, again the raw (not low-passed) audio is shared here.</li> </ul> <p><strong>'Acoustic Indices':</strong> contains the CSV files with matrices of the Acoustic Indices extracted from the first channel of the low-passed '6mic Audio O1O2' recordings.</p> <ul> <li>Each column is for a different index, rows are the values over time (indices were extracted on 30 s windows).</li> <li>We extracted the following 7 common acoustic indices: <ul> <li>Acoustic Complexity Index (ACI; column 1),</li> <li>Acoustic Diversity Index (ADI; column 2),</li> <li>Acoustic Evenness (AEve; column 3),</li> <li>Bioacoustic Index (Bio; column 4),</li> <li>Normalised Difference Soundscape Index (NDSI; column 5)</li> <li>Acoustic Entropy (H; column 6), and</li> <li>Median of the Amplitude Envelope (M; column 7).</li> <li>Filenames indicate: <ul> <li>the field site,</li> <li>whether the indices are for a field or VSE ('Lab') recording, and</li> <li>the orientation of the recording for the latter ('Vert', '45', or 'H').</li> <li>The 'LP' suffix denotes that these indices were extracted from a low-passed version of the 6mic O1O2 recordings (4 kHz cutoff and 12 dB roll-off).</li> </ul> </li> </ul> </li> </ul> <p><strong>'BirdNET O1O2 Outputs':</strong> CSV files generated from avian call classifier BirdNET (using the Winows GUI version) using the first channel ('Mic 1') of the '6mic Audio O1O2' recordings.</p> <p><strong>'BirdNET O3 Outputs':</strong> outputs of BirdNET on the '6mic Audio O3' recordings.</p> <ul> <li>Here, rather than the raw CSV files generated by BirdNET, the BirdNET results have been filtered to just those during the added bird calls' start and end times, and have been compiled into two CSV files: <ul> <li>one for recordings of VSE-based soundscapes with bird calls added by <em>ambisonic encoding</em> and</li> <li>another for calls added by <em>individual loudspeaker playback</em>.</li> </ul> </li> <li>These CSV files also contain columns to specify added birds' site, sweep (used to generate reverberation, see '6mic Audio O3' above), azimuth and elevation.</li> </ul> <p><strong>'HARKBird O1O2 Outputs':</strong> outputs of avian call localisation tool HARKBird on the '6mic Audio O1O2' recordings. Note that HARKBird outputs a folder with additional results for each file, however, only the CSV files presented here. These contain the times and estimated azimuth angles of bird calls and were the only HARKBird ouput used for subsequent analyses.</p> <ul> <li>Filenames indicate whether recordings are from the field ('Field') or VSE ('ReRec'), and</li> <li>those for the latter also indicate the recording orientation ('Vert', 45' 'H').</li> <li>The final number in each filename indicates the site the recording corresponds to.</li> </ul> <p><strong>'HARKBird O3 Outputs':</strong> CSV files of HARKBird outputs (as described above) for the '6mic Audio O3' recordings.</p> <ul> <li>Filenames are based on those for this set of recordings (see '6mic Audio O3' above);</li> <li>names that contain 'LP' were low-passed (with a 4 kHz cutoff frequency and 12 dB roll-off) prior to passing through HARKBird.</li> </ul> <p><strong>'Manual Labels O2':</strong> manual labels of audible bird calls' species in the omnidirectional (first) channel of the Zylia ZM-1 ambisonic recordings (see 'Zylia Recordings' below) for all sites. This data was used to calculate the precision and recall of BirdNET's outputs on the 6mic O1O2 recordings for these sites.</p> <p><strong>'VGGish Features':</strong> contains the 128-dimension feature embedding of the pre-trained VGGish convolutional neural network extracted from the '6mic Audio O1O2' recordings.</p> <ul> <li>Filenames indicate the site,</li> <li>whether the recording was made in the field or VSE ('Lab'),</li> <li>and the recording's orientation.</li> <li>Note again that recordings were low-passed (4 kHz cutoff and 12 dB roll-off) prior to the feature extraction, hence the 'LP' suffix.</li> </ul> <p><strong>'Zylia Recordings':</strong> approximately 10 minute field recordings of the 6 study sites captured with the 19-microphone ZYLIA 'ZM-1' recorder. These recordings have been converted to third order ambisonic 'b-format' (16 channels) using Furse-Malham channel ordering and SN3D normalisation. This was achieved with the 'Zylia Ambisonics Converter' software. These third-order ambisonic recordings were used to replicate the six sites' soundscapes in the VSE.</p> <p>The accompanying code for this dataset has been submitted via ScholarOne with the manuscript for peer-review.</p>
MONITOR PhysiCube Dataset: Dataset of Physiological Parameters for Monitoring Stress in Garments Workers using PhysiCube Sensors
<h3><strong>MONITOR: A Data-driven Intelligent MONITORring System to Improve Quality of Working Life</strong></h3> <p>The project, <strong>MONITOR</strong>, aims to establish mobility of bilateral collaboration between<strong> Islamic University of Technology (IUT), Bangladesh</strong>, and <strong>Mälardalen University (MDH), Sweden</strong> in specific research and education domains of Science and Technology at an institutional level. MDH has been leading several projects to develop tools to classify physiological data for individualized classification of physiological sensor readings to monitor stress, fatigue, and mental load at work and home environments. The project was funded by <strong>Stiftelsen för internationalisering av högre utbildning och forskning (STINT) </strong>under the <strong>Grant Agreement No. 202100-2916</strong>.</p> <p>This project has unfolded many benefits for both the partnering universities. It gave access to a wide variety of data regarding mental health-related parameters to the researchers. Given the data collection was done in Bangladesh, it was challenging for the researchers to extract correct and accurate data under harsh/unsafe conditions. This trained the researchers to collect data under real complex conditions. In this project, MDH provided resources and knowledge to update the already developed AI algorithms to support Garment workers’ mental health monitoring. IUT provided expert human resources to develop a sensor-setting framework and assisted in collecting and analyzing the data.</p> <p><strong>Northern Tosrifa Group (NTG):</strong> A LEED-certified garment factory in Bangladesh, provided the test environment and subjects to collect and validate the system. The group has 5 companies under its operation and all the companies manufacture and export their products to markets in North America and Europe. For the project MONITOR, <strong>Fashion Asia Limited (FAL) </strong>(one of the 5 companies under NTG) provided the necessary manpower and data collection environment for the project. </p> <h3><strong>About the Dataset</strong></h3> <p>As part of the MONITOR project, an in-house sensor device, named <strong>PhysiCube</strong>, was developed for measuring the following physiological parameters-</p> <ol> <li><strong>Object Temperature(F): </strong>The skin temperature of the subject (measured in Fahrenheit) was recorded using a low-cost off-the-shelf temperature sensor.</li> <li><strong>Heart Rate Ear(BPM): </strong>The heart rate of the subject (measured in Beats per Minute) was recorded using a heart rate sensor wearable on the ear lobe.</li> <li><strong>Galvanic Skin Response (GSR): </strong>The Galvanic Skin Response (GSR), which measures the skin’s electrical conductivity in response to sweat gland activity in the fingers and palms was measured using a low-cost off-the-shelf GSR Sensor.</li> </ol> <p>The dataset contains the timestamped values (as elapsed time, in the format '<strong>%MM:%SS</strong>') of all the aforementioned parameters for 24 subjects <strong>(29.17% Male, 70.83% Female, Age Group: 18-40 years)</strong> with written consent.</p> <p>Data were collected in the following three phases-</p> <ol> <li><strong>Baseline Values:</strong> Values of the aforementioned physiological markers that reflect the corresponding values under normal/relaxed conditions. In other words, the stress-free values of these physiological markers. Example filename: <strong>C01_PF_base.csv</strong>.</li> <li><strong>After-Lunch Values: </strong>Values that were recorded after the lunch break. Example filename: <strong>C10_PM_after_lunch.csv</strong>.</li> <li><strong>Stress-Induced Values: </strong>Values that were recorded while inducing stress using various stress-inducing questionnaires available in the literature. Example filename: <strong>C10_PM_stress_induced.csv</strong>.</li> </ol> <p>Files were organized in a way such that each subject had a directory. In each directory, files were named as '<strong><subject_id>_<device_tag><gender_tag>_<category>.csv</strong>'. For example:</p> <ol> <li><strong>Base</strong> data of a Female subject <strong>C01</strong> recorded using a <strong>PhysiCube </strong>sensor will have the name: <strong>C01_PF_base.csv</strong></li> <li><strong>After Lunch</strong> data of a Male subject <strong>C10</strong> recorded using a <strong>PhysiCube </strong>sensor will have the name: <strong>C10_PM_after_lunch.csv</strong></li> </ol> <p><strong>N.B. :</strong> As part of the <strong>MONITOR </strong>project, another <a href="../doi/10.5281/zenodo.11523282" target="_blank" rel="noopener">dataset</a> titled, "<strong>MONITOR Shimmer Dataset: Dataset of Physiological Parameters for Monitoring Stress in Garments Workers using Shimmer Sensors</strong>", was simultaneously developed using <strong>Shimmer </strong>sensors, to measure the physiological data of the same subjects.</p>
MONITOR Shimmer Dataset: Dataset of Physiological Parameters for Monitoring Stress in Garments Workers using Shimmer Sensors
<h3><strong>MONITOR: A Data-driven Intelligent MONITORring System to Improve Quality of Working Life</strong></h3> <p>The project, <strong>MONITOR</strong>, aims to establish mobility of bilateral collaboration between<strong> Islamic University of Technology (IUT), Bangladesh</strong>, and <strong>Mälardalen University (MDH), Sweden</strong> in specific research and education domains of Science and Technology at an institutional level. MDH has been leading several projects to develop tools to classify physiological data for individualized classification of physiological sensor readings to monitor stress, fatigue, and mental load at work and home environments. The project was funded by <strong>Stiftelsen för internationalisering av högre utbildning och forskning (STINT) </strong>under the <strong>Grant Agreement No. 202100-2916</strong>.</p> <p>This project has unfolded many benefits for both the partnering universities. It gave access to a wide variety of data regarding mental health-related parameters to the researchers. Given the data collection was done in Bangladesh, it was challenging for the researchers to extract correct and accurate data under harsh/unsafe conditions. This trained the researchers to collect data under real complex conditions. In this project, MDH provided resources and knowledge to update the already developed AI algorithms to support Garment workers’ mental health monitoring. IUT provided expert human resources to develop a sensor-setting framework and assisted in collecting and analyzing the data.</p> <p><strong>Northern Tosrifa Group (NTG):</strong> A LEED-certified garment factory in Bangladesh, provided the test environment and subjects to collect and validate the system. The group has 5 companies under its operation and all the companies manufacture and export their products to markets in North America and Europe. For the project MONITOR, <strong>Fashion Asia Limited (FAL) </strong>(one of the 5 companies under NTG) provided the necessary manpower and data collection environment for the project. </p> <h3><strong>About the Dataset</strong></h3> <p>As part of the MONITOR project, <strong>27</strong> subjects <strong>(29.17% Male, 83.33% Female, Age Group: 18-40 years) </strong>were recruited with written consent to collect stress-related physiological parameters. However, data from <strong>24 </strong>subjects <strong>(29.17% Male, 70.83% Female, Age Group: 18-40 years)</strong> were considered due to inconsistencies in the data from <strong>3 </strong>subjects owing to measurement inconsistencies, and device and/or human errors. <strong>Shimmer</strong> sensors were used for measuring the following physiological parameters-</p> <ol> <li><strong>Heart Rate Ear(BPM): </strong>The heart rate of the subject (measured in Beats per Minute) was recorded using the embedded heart rate sensor in the Shimmer module, wearable on the ear lobe.</li> <li><strong>Galvanic Skin Response (GSR): </strong>The Galvanic Skin Response (GSR), which measures the skin’s electrical conductivity in response to sweat gland activity in the fingers and palms, was measured using the embedded GSR Sensor in the Shimmer module.</li> </ol> <p>The dataset contains the timestamped values (as elapsed time, in the format '<strong>%MM:%SS</strong>') of all the parameters above.</p> <p>Data were collected in the following three phases-</p> <ol> <li><strong>Baseline Values:</strong> Values recorded under stress-free conditions (at the beginning of the working shift of a garment worker). Example filename: <strong>C01_SF_base.csv</strong>.</li> <li><strong>After-Lunch Values: </strong>Values recorded after the lunch break. Example filename: <strong>C10_SM_after_lunch.csv</strong>.</li> <li><strong>Stress-Induced Values: </strong>Values recorded while inducing stress using various stress-inducing questionnaires available in the literature. Example filename: <strong>C10</strong>_<strong>SM_stress_induced.csv</strong>.</li> </ol> <p>Files were organized in a way such that each subject had a directory. In each directory, files were named as '<strong><subject_id>_<device_tag><gender_tag>_<category>.csv</strong>'. For example:</p> <ol> <li><strong>Base</strong> data of a <strong>Female </strong>subject <strong>C01</strong> recorded using a <strong>Shimmer</strong> sensor will have the name: <strong>C01_SF_base.csv</strong></li> <li><strong>After-Lunch</strong> data of a <strong>Male </strong>subject <strong>C10</strong> recorded using a <strong>Shimmer </strong>sensor will have the name: <strong>C10_SM_after_lunch.csv</strong></li> </ol> <p><strong>N.B. :</strong> As part of the <strong>MONITOR </strong>project, another <a href="https://doi.org/10.5281/zenodo.11518751" target="_blank" rel="noopener">dataset</a> titled, "<strong>MONITOR PhysiCube Dataset: Dataset of Physiological Parameters for Monitoring Stress in Garments Workers using PhysiCube Sensors</strong>", was simultaneously developed using an in-house sensor device, named <strong>PhysiCube</strong>, to measure the physiological data of the same subjects.</p>
Earthquake catalog in QuakeML format from: "Local earthquake monitoring with a low-cost seismic network: a case study in Nepal"
<p>Earthquake catalog of the microseismicity in central Nepal recorded by a Low cost seismic network(Raspberry Shake) in 2021 in QuakeML format. The information included for each event contains location, phase pick, local magnitude information. </p>
Diffusion-based subsurface multiphysics monitoring and forecasting
<p><span>We develop a novel monitoring and forecasting framework using video diffusion </span><span>models of subsurface changes due to, for example, CO</span><span>2</span> <span>injection. In this letter, we present the relevant dataset to the paper. </span></p>
Green Transition Monitoring Framework for WB (first draft)
<p><span>This dataset comprises a set of indicators for measuring or assessing green transition in Western Balkan countries, based on the research cases developed through the GreenFORCE project. The indicators will be validated with local stakeholders and re-published as a Report on Monitoring Green Transition in WB.</span></p>
Size matters in quantitative radar monitoring of animal migration: estimating monitored volume from wingbeat frequency
<p>Echoes used for the publication entitiled "Size matters in quantitative radar monitoring of animal migration: estimating monitored volume from wingbeat frequency" submitted to Ecography for the ENRAM special issue.</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.