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116 results for “research activities”
Bonanza Creek LTER: Hourly Photosynthetically Active Radiation (PAR) Measurements from 1988 to Present in the Bonanza Creek Experimental Forest and the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska
PAR sensors measure solar radiation in the 400 to 700 nm waveband in microEinsteins per sq. m. per second. The average hourly reading is converted to microEinsteins per sq. m. per hour and summed over 24 hours to obtain daily Einsteins per sq. m. Sensors are removed at the end of September for annual calibrations and replaced back at the sites by the first of May each year.
Beyond the passive–active dichotomy: aligning research with the intervention continuum framework of ecological restoration
<p>The dataset and R script file are associated with the publication, "Krishnan, A. and Osuri, A.M. (2022), Beyond the passive-active dichotomy: aligning research with the intervention continuum framework of ecological restoration. Restoration Ecology:e13828. https://doi.org/10.1111/rec.13828"</p> <p>Please refer to the readme.txt file for more information on the dataset and analyses.</p>
Questionnaire for the self-assessment of digital preservation activities in institutional research repositories
<p>This dataset includes a questionnaire designed to enable institutional repository managers to conduct a self-assessment of their digital preservation strategies and activities. It consists of 46 evaluation criteria extracted and modified from the NDSA Levels of Digital Preservation and ISO 16363:2017 standards. The questionnaire is provided in queXML format, facilitating its import into various survey applications</p> <p> </p>
WSSSPE5.1 presentations: Research software sustainability activity analysis
<p>Distribution of actors, actions and actees from <a href="https://www.slideshare.net/danielskatz/research-software-sustainability-wssspe-urssi">Daniel S. Katz' schematic of research software sustainability</a> over presentations given at the Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE5.1) on 6 September 2017 in Manchester, UK (Proceedings: <a href="https://doi.org/10.6084/m9.figshare.c.3869782.v3">https://doi.org/10.6084/m9.figshare.c.3869782.v3</a>).</p>
COVRIN D0.3.1: Database of COVID-19 research activities
<p>OHEJP project: COVRIN "One Health research integration on SARS-CoV-2 emergence, risk assessment and preparedness".</p> <p>Since the start of the pandemic in early 2020, a huge number of research projects have been initiated on SARS-CoV-2/COVID-19; additionally, many pre-existing networks and infrastructures have turned their attention to the virus, setting up SARS-CoV-2-specific services. To avoid overlaps and ensure optimal use of resources, a scoping review was performed of European Union-supported SARS-CoV-2 research activities that overlap with COVRIN in terms of focus.</p> <p>This database is associated with OHEJP Deliverable report "D0.3.1: Scoping review of European Union-supported COVID-19 research activities" available at https://doi.org/10.5281/zenodo.5537781</p>
Carbon Dynamics Along a Permafrost Gradient at Caribou-Poker Creeks Research Watershed (CPCRW) in Interior Alaska: Active Layer Depth (ALD) in a 75x75m spatial domain along a permafrost and vegetation gradient.
This dataset includes active layer depth (ALD) data across a 75x75m spatial domain in the Caribou-Poker Creeks Research Watershed. Project summary: Specific leaf area (SLA, leaf area per unit dry mass) is a key canopy structural characteristic, a measure of photosynthetic capacity, and an important input into many terrestrial process models. Although many studies have examined SLA variation, relatively few data exist from high latitude, climate-sensitive permafrost regions. We measured SLA and soil and topographic properties across a boreal forest permafrost transition, in which forest composition changed as permafrost deepened from 54 to >150 cm over 75 m hillslope transects in Caribou-Poker Creeks Research Watershed, Alaska. This is an exploratory study to begin understanding SLA variation and controls thereof in a non-contiguous permafrost system.
Research dat for: "Active Tectonics of the North Tunisian Continental Margin"
<p>The data are the figures in high-resolution for the article: "Active Tectonics of the North Tunisian Continental Margin"</p>
Research Data for Research Activity "Virtual Climatization"
<p>Machining workpieces on machine tools is an inherently energy intensive process. To achieve ever increasing tolerance requirements, thermal disturbances on machine tool and workpiece and resulting thermo-elastic deformations need to be addressed. Instead of cooling whole shopfloor environments and machines, desensitizing the process against temperature changes has the potential to reduce the carbon footprint of machining processes.</p> <p>To develop models for machine tool error prediction due to temperature, a comprehensive and diverse data set is essential. It includes data sources that capture relevant information about the machine tool's thermal behavior and its corresponding error. Hence, an experiment moving a machine tool with a fixed path is run for several hundred times with the same path for each run. Simultaneously, the actual machine position is measured using a laser tracker and component temperature is tracked.</p> <p> </p> <p><a href="https://git-ce.rwth-aachen.de/mark.sanders/bzt-data">https://git-ce.rwth-aachen.de/mark.sanders/bzt-data</a></p>
Eight Mile Lake Research Watershed, Thaw Gradient: Active Layer thickness 2004-2013.
In this larger study, we are asking the question: Is old carbon that comprises the bulk of the soil organic matter pool released in response to thawing of permafrost? We are answering this question by using a combination of field and laboratory experiments to measure net carbon exchange, radiocarbon isotope ratios in soil organic matter, soil respiration, and dissolved organic carbon, in tundra ecosystems. The objective of these proposed measurements is to develop a mechanistic understanding of the SOM sources contributing to C losses following permafrost thawing. We are making these measurements at an established tundra field site near Healy, Alaska in the foothills of the Alaska Range. Field measurements center on a natural experiment where permafrost has been observed to warm and thaw over the past several decades. This area represents a gradient of sites, each with a different degree of change due to permafrost thawing. In this area, we eatablished three sites with different degrees of disturbance from the permafrost thaw: 1) a relatively undisturbed tussock tundra (Minimal Thaw site), 2) a site adjacent to to the borehole which is monitoring permafrost thaw since 1985 with intermediate degree of disturbance (Moderate thaw site) and 3) a site where permafrost thaw apeared to have started more than three decades ago (Extensive thaw site) As such, this area is unique for addressing questions at the time and spatial scales relevant for change in arctic ecosystems. This data set includes the active layer depth (ALD), which is the maximum seasonal thaw depth from each of the three sites mentioned before.
IPBES Data Management Tutorials - Session 4.1: General introduction to the management of active research data
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>Data management of active research data </em>chapter will provide an introduction for IPBES experts on how to manage data while actively being used, analyzed, and produced to fulfill the criteria of the IPBES data management policy.</p> <p>This session,<em> General introduction to the management of active research data</em>, covers what active data management is, why it is important, and considerations for key decisions made in this process. </p>
Research data for: Patchy energy landscapes promote stability of small groups of active particles
<p>Research data supporting the pubblication "Patchy energy landscapes promote stability of small groups of active particles"</p>
Research data for the article "Membrane-Active Antibiotics Affect Domains in Bacterial Membranes as the First Step of Their Activity" by Adéla Melcrová, Christiaan Klein, and Wouter Roos, Nano Letters 2024.
<p>Research data for the publication Membrane-Active Antibiotics Affect Domains in Bacterial Membranes as the First Step of Their Activity, Nano Letters, 2024. <a href="https://doi.org/10.1021/acs.nanolett.4c01873" target="_blank" rel="noopener">https://doi.org/10.1021/acs.nanolett.4c01873</a></p> <p>The data set contains experimental data on the study of antimicrobial N-alkylamide 3d and its interaction with the membrane extracted from the bacteria Staphylococcus aureus. Experimental details can be found in the Methods section of the Supporting Information of the mentioned article. </p> <p>The data set contains underlying data for each Figure in the publication including the figures in the Supporting Information. </p> <ul> <li>Figure 1, S2, S3, S6, S8 - Atomic Force Microscopy images in the raw format as collected on JPK Nano Wizard Ultra Speed AFM.</li> <li>Figure 2, 3, S1 - High-Speed Atomic Force Microscopy images in the raw format as collected on high-speed AFM RIBM machine. The data could be opened in Kodec software. The full trajectories recorded on the high-speed AFM machine are presented here. Only snapshots were used in the publication Figures.</li> <li>Figure S7, S9 - Snapshots from the High-Speed AFM measurements showing the cross-section lines (.bmp) together with the cross-section profiles (.csv) that were used to measure the height of supramolecular structures on top of the bacterial membrane.</li> <li>Data underlying Figure S5 and Supporting Video 1 overlap with the data presented in Figure 2a.</li> </ul>
Dataset relating the Social activity of Open Research Data on ResearchGate
<p>The potential of Open Research Data (ORD) within the context of open science and digital scholarship can be frustrated if data remains unused. Although current research has investigated the way ORD is being published, researchers’ behaviour of ORD publishing and sharing on academic social networks (ASN) remains insufficiently explored. The research to which this dataset is connected aims to illustrate some parameters of social activity around self-archived ORDs on ResearchGate. The study analyses whether the ORDs publication leads to social activity (reads and citations) around the ORDs and their linked published articles, including eventual associations between the social activity and the researchers’ profile (scientific domain, gender, region, professional position, reputation) as well as the quality of the ORD published.</p> <p>The current dataset is composed by:</p> <p>A- The .csv file, extracted as a random sample of 752 ORD items from ResearchGate. The dataset has been polished and anonymized. The variables relating the researchers' profiles and citations to the ORD lined research were got from the researchers' profiles and published research. However, for the purpose of anonymisation, these variables have been coded and the original information removed.</p> <p>B- The codebook explaining the variables and metrics contained in the file (A)</p> <p>C- The R script. This script contains a number of explorations not reported in the final paper. The quantitative techniques applied include descriptive statistics, logistic regression and K-means cluster analysis.</p> <p>D- Five tables, three figures and two annexes (Logistic Regression I and II) created over the basis of the dataset (A)</p> <p>The results have been interpreted in terms of three main aspects.</p> <ul> <li>Firstly, there is still an underdeveloped social activity around self-archived ORD in ResearchGate (operationalized as reads and citations) overall and in spite of the published ORDs quality.</li> <li>Secondly, it was found an uncovering of the relevance of the moderating effects over ORD, which spots traditional dynamics within the “innovative” practice of engaging with data practices.</li> <li>Thirdly, a rather similar situation of ResearchGate as ASN with regard to other data platforms and repositories in terms of social activity around ORD was detected.</li> </ul> <p>The potential of Open Research Data (ORD) within the context of open science and digital scholarship can be frustrated if data remains unused. </p>
Datasets for "Trapdoor fault activation: a step towards caldera collapse at Sierra Negra, Galápagos, Ecuador", Journal of Geophysical Research: Solid Earth
<p>The following files were used in the analysis from "Trapdoor fault activation: a step towards caldera collapse at Sierra Negra, Galápagos, Ecuador", <em>Journal of Geophysical Research: Solid Earth</em>:</p> <p><strong>alos2_csk/alos2_sm1_dsc_20180504_20180713/:</strong> Includes DEM used in processing of the ALOS-2 SM1 descending interferogram spanning 4 May 2018–13 July 2018 (dem.2alks_2rlks.crop.*); geocoded SAR offsets in pixels (range resolution=1.43 m/pixel; azimuth resolution=2.01 m/pixel; denseOffsets.bil.2alks_2rlks.geo*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.2alks_2rlks.geo.*); geocoded, unwrapped interferometric phase (filt_topophase.unw.2alks_2rlks.geo.*); geocoded incidence and heading angle for the interferogram (los.rdr.2alks_2rlks.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/alos2_sm3_asc_20180114_20180701/:</strong> Includes DEM used in processing of the ALOS-2 SM3 ascending interferogram spanning 14 January 2018–1 July 2018 (dem.crop.*); geocoded, unwrapped interferometric phase (filt_topophase.unw.geo.*); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/alos2_wd1_dsc_147_180518_180629/</strong>: Includes DEM used in processing of the ALOS-2 WD1 descending interferogram spanning 18 May 2018–29 June 2018 (crop.dem.*); geocoded, unwrapped interferometric phase (filt_180629-180518_2rlks_14alks.unw.geo.*) ; geocoded incidence and heading angle for the interferogram (180629-180518_2rlks_14alks.los.geo.*); geocoded coherence for the interferogram (180629-180518_2rlks_14alks.cor.geo.*); geocoded mask for the interferogram (filt_topophase.unw.masked.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_asc_20180617_20180703/:</strong> Includes DEM used in processing of the COSMO-SkyMed ascending interferogram spanning 17 June 2018–3 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.54 m/pixel; azimuth resolution=2.48 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_asc_20180703_20180719/</strong>: Includes DEM used in processing of the COSMO-SkyMed ascending interferogram spanning 3 July 2018–19 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.54 m/pixel; azimuth resolution=2.48 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_dsc_20180618_20180704/:</strong> Includes DEM used in processing of the COSMO-SkyMed descending interferogram spanning 18 June 2018–4 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.70 m/pixel; azimuth resolution=2.45 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_dsc_20180704_20180720/: </strong>Includes DEM used in processing of the COSMO-SkyMed descending interferogram spanning 4 July 2018–20 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.70 m/pixel; azimuth resolution=2.45 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>S1.zip</strong>: Unwrapped, geocoded interferometric phase in meters for Sentinel-1 ascending and descending interferograms, spanning time periods of interest. </p> <p><strong>S1_20180630_20180706_asc_mask_nan_ref.grd: </strong>Unwrapped, geocoded, and masked interferometric phase for Sentinel-1 ascending interferogram spanning 30 June 2018–6 July 2018.</p> <p><strong>S1_20180701_20180707_desc_mask_nan_ref.grd: </strong>Unwrapped, geocoded, and masked interferometric phase for Sentinel-1 descending interferogram spanning 1 July 2018–7 July 2018.</p> <p><strong>tandemx12m_crop.grd</strong>: TanDEM-X 12 meter DEM in meters.</p> <p><strong>pleaides_tandemx12m_diff.grd</strong>: Difference between the TanDEM-X 12 meter DEM and the Pléiades-derived DEM, computed from images on 29 October 2018 and 6 December 2019.</p> <p><strong>trapdoorFaultSlip.zip</strong>: Discretized trapdoor fault patch dip-slip modeled to fit deformation from Sentinel-1 ascending interferograms, estimated using the Classic Slip Inversion software.</p> <p><strong>trapdoorFaultTraces.zip</strong>: Caldera and trapdoor fault traces, derived from <em>Bell et al. 2021</em>.</p> <p><strong>SN14_tilt_10s_2018-19.txt</strong>: Text filt containing date-time (sampled at 10 s, in matplotlib date-time number format), N-S tilt and E-W tilt. Tilt values can be converted to microradians by multiplying by a factor of 0.00129. Tilt data obtained from authors of <em>Bell et al. 2021</em>. For use of this dataset, please cite <a href="https://doi.org/10.1038/s41467-021-21596-4">https://doi.org/10.1038/s41467-021-21596-4</a>.</p>
Data from: Thermal springs and active fault network of the central Colca River basin, Western Cordillera, Peru, published in Journal of Volcanology and Geothermal Research
<p>We used hydrogeochemical analysis of 35 water samples from springs and geysers, together with isotopic (δ<sup>18</sup>O and δD) analysis, chemical and mineral studies of precipitates collected in the field around these outflows, and field observations to study the thermal system of the Colca River basin in S Peru. We aimed to determine the geochemistry of thermal waters, identify fluid sources and their origin, estimate reservoir temperature, and discuss the regional tectonic and volcanic framework. Our findings presented in Tyc et al. (2022; https://doi.org/10.1016/j.jvolgeores.2022.107513) corroborate a heterogeneous and complex geothermal system in the central region of the Colca River basin. This system exhibits contrasting hydrogeochemical and physical characteristics, variable isotope compositions, distinct reservoir temperatures, and associated precipitates near thermal springs. The control of water chemistry in this area is closely linked to the activity of the Ampato-Sabancaya magmatic chamber and the presence of tectonic structures, which enable intricate interactions between meteoric waters, magmatic fluids, and gases.</p> <p>Here, we present datasets used in the article (Tyc et al., 2022; https://doi.org/10.1016/j.jvolgeores.2022.107513), including:</p> <p>- Physicochemical characteristics of water samples collected by authors in the field in September 2012 and August–September 2017 (Table 1)</p> <p>- Chemical and isotopic composition of water samples collected by authors in the field in September 2012 and August–September 2017 (Table 2) and those monitored by INGEMMET in years 2013-2018 (Table 3)</p> <p>- Chosen molecular ratios discussed in Tyc et al., 2022 (Table 4)</p> <p>- Calculated reservoir temperature with the use of different Na/K geothermometers (Table 5)</p> <p>- Mineral phases in efflorescences precipitating at the water sampling sites (Table 6).</p> <p>Thirty-five sets of water samples were collected in the field in September 2012 and August–September 2017 using polyethylene bottles of high density (Table 1). Consequently, these were analyzed in the Water Analysis Laboratory at the University of Silesia in Katowice (Poland; Table 2). Water temperatures, pH, and electrical conductivity were measured in the field using portable pH meter CP-315 and conductivity meter CC-315, both with temperature sensors, with an accuracy of ±0.1 °C, ±0.01 pH, and ± 0.1% (up to 19.999 mS/cm) or ± 0.25% (above 20.00 mS/cm), respectively. Discharge of springs was estimated if possible (Table 1). Both cations and anions were analyzed by ion chromatography using Methron 850 Professional Ion Chromatograph with separate Metrosept C4–150 and A-supp 7–250 columns for cations and anions, respectively (Tables 2 and 4). Analysis of water analyses collected by INGEMMET in years 2013-2018 was performed at the INGEMMET Chemical Laboratory in Lima with the use of ion chromatography (Dionex ICS 5000) for the determination of anions and inductively coupled plasma optical emission spectrometry (ICP-OES) – VARIAN for cations (Table 3). Isotopic analyses (δ<sup>2</sup>H, δ<sup>18</sup>O) of 17 water samples collected in 2017 were performed at the Stable Isotope Laboratory Institute of Geological Sciences Polish Academy of Sciences (Table 2). The δ<sup>2</sup>H values of studied H<sub>2</sub>O were measured using the H-Device peripheral coupled to MAT 253 IRMS (Thermo Scientific) in a dual inlet system. For the determination of δ<sup>18</sup>O in H<sub>2</sub>O, an equilibration technique was used. The analysis used the GasBench II peripheral device (Thermo Scientific) coupled to MAT 253 IRMS with a continuous He flow. The AquaChem 4.0.284 software was used to evaluate the water samples' geochemical properties and calculate reservoir temperature for thermal waters (Table 5). Precipitates found at the water sampling sites were collected separately into plastic bags with strings and sealed boxes. These samples were subsequently analyzed at the Institute of Earth Sciences, University of Silesia in Katowice. The qualitative chemical composition and mineral characteristics were examined using a Philips XL 30 ESEM/TMP scanning electron microscope coupled with an energy-dispersive spectrometer (EDS; EDAX type Sapphire). The phase composition of the precipitates was determined through X-ray diffraction (XRD) using a Philips PW 3710 diffractometer. The XRD data were analyzed and interpreted using the X'Pert HIGHScore Plus software (Table 6).</p>
Digital research data from: Magnesium activation affects the properties and phosphate sorption capacity of poultry litter biochar
<p>This is digital research data corresponding to a published manuscript, <em>Magnesium activation affects the properties and phosphate sorption capacity of poultry litter biochar</em>. Biochar 5:64. https://doi.org/10.1007/s42773-023-00263-5. </p> <p>Biochars with a high affinity for phosphorus (P) are promising soil amendments for reducing P in agricultural runoff. Poultry litter (PL) is an abundant biochar feedstock. However, PL-derived biochars are typically high in soluble P and therefore require chemical modification to become effective P sorbents. This study investigated the effect of magnesium (Mg) activation on extractable P (EP) and P sorption capacities of PL-derived biochars. Biochar was produced at 500–900 °C from PL activated with 0–1 M Mg. Three differentially aged PL feedstocks were evaluated (1-, 3–5-, and 7–9-year-old). </p>
Flares of Low Back Pain With Activity Research Study
ClinicalTrials.gov study NCT04828330. IPD Sharing: YES. Countries: 1. Publications: 1.
Seniors Health and Activity Research Program-Pilot
ClinicalTrials.gov study NCT00688155. IPD Sharing: YES. Countries: 1. Publications: 5.
Digital research data from: Magnesium activation affects the properties and phosphate sorption capacity of poultry litter biochar
Open the record for dataset details and reuse information.
Research Data for FiHi: Fusion of inertial and high-resolution acoustic data for privacy-preserving human activity recognition
<h1><strong>Description</strong></h1> <div>This dataset contains information on 20 different activities collected from 15 participants (20-55 years old) using Wit-motion smart inertial sensors and Double Acoustics guitar pickups. Each participant performs these daily activities in an unrestricted environment, with each activity lasting at least 60 seconds and repeated twice.</div> <div> </div> <div>If you use the dataset in an academic work, please cite: </div> <div> </div> <div><code>@ARTICLE{10980212,</code><br><code> author={Yang, Zhe and Zhang, Ying and Li, Yanjun and Huang, Linchong and Hu, Ping and Lin, Yuexiang},</code><br><code> journal={IEEE Transactions on Instrumentation and Measurement}, </code><br><code> title={Fusion of Inertial and High-resolution Acoustic Data for Privacy-Preserving Human Activity Recognition}, </code><br><code> year={2025},</code><br><code> volume={74},</code><br><code> number={},</code><br><code> pages={1-20},</code><br><code> keywords={Human activity recognition;Sensors;Acoustics;Feature extraction;Privacy;Microphones;Biomedical monitoring;Wireless fidelity;Sensor phenomena and characterization;Sensor fusion;Human activities recognition;inertial sensing;Hi-res audio;attention mechanism},</code><br><code> doi={10.1109/TIM.2025.3565250}}</code></div> <h1><strong>DataSet Information</strong></h1> <h2><strong>1.Original_data.zip</strong></h2> <div>The data was annotated by manually reviewing the audio clips and assigning appropriate activity labels. Timestamping the inertial sensor data with the start time of the audio device recorded by the experimenter ensured correct segmentation and synchronization of the inertial and acoustic data. The total data length for</div> <div>all participants is over 10 hours.</div> <h3><strong>(1) </strong><strong>IMU</strong><strong> DATA</strong></h3> <div>The inertial data (accelerometer and gyroscope) is sampled at 100 Hz and transmitted by Bluetooth to the host computer. These reviewed and annotated original samples from 15 participants are placed in separate csv files. The arrangement of information in each csv file is:</div> <div>Col 1-3: 3D-acceleration data (g)</div> <div>Col 4-6: 3D-gyroscope data (°/s)</div> <h3><strong>(2) Audio DATA</strong></h3> <div>The acoustic data are sampled at 192 kHz by a Steinberg sound card and transmitted by USB cable to the host computer. These original samples are placed in separate wav files, with each file name containing all the necessary information regarding the contents of the file.</div> <div><strong>For example:</strong></div> <div>100801_sitting</div> <div>Participant ID (1-4 digits): 1008, Session ID (5-6 digits): 01, Activity ID: sitting.</div> <div> </div> <h2><strong>2、Processed_data.zip</strong></h2> <div>The last two columns of each file are as follows:</div> <ul> <li> <div>participant_id: such as 100101, 100102, 100201 ...... The last two digits are the Session ID, representing the two sessions from the same participant for the same activity.</div> </li> <li> <div>activity_id: Refer to the ACTIVITY SET below</div> </li> </ul> <h3><strong>(1) </strong><strong>IMU</strong><strong> DATA</strong></h3> <div>The original inertial data is individually aligned with the processed Audio data based on their start times, with any excess data rows at the end being trimmed. Then, these inertial data files are augmented with activity_id and participant_id for identification, and consolidated into a single csv file.</div> <div>The continuous motion signal is segmented into sliding windows, each with a duration of 3 seconds and a step size of 3 seconds. Given the IMU’s sampling rate of 100 Hz, each window of inertial data consists of 300 time steps, with 6 channels of information (3 axes each for accelerometer and gyroscope). Consequently, a single inertial sample is represented by a 300 × 6 dimensional matrix.</div> <h3><strong>(2) Audio DATA</strong></h3> <div>The acoustic signals are processed using the Short Time Fourier Transform (STFT) with a window length of 1024 points and an overlap of 256 points, , which generates n linear spaced frequency bins between n kHz frequency range in the frequency domain. The output contains the estimate of the short-term, time-localized frequency patterns. We examine two levels of privacy protection: 8 ∼ 96 kHz for non-speech sound and 20 ∼ 96 kHz for inaudible sound, with 88 and 76 linear spaced frequency bins, respectively.</div> <div>Under a sample rate of 192 kHz for the original acoustic signal, there are (192000 - 256)/(1024 - 256) ≈ 250 frequency features within a second, while each feature has 88 and 76 dimensions for non-speech (8∼96 kHz) and inaudible (20∼96 kHz) feature, respectively.</div> <h1><strong>ACTIVITY </strong><strong>SET</strong></h1> <div>The activityIDs and corresponding activities are listed in the following:</div> <div>0: use microwave</div> <div>1: brush teeth</div> <div>2: browse video</div> <div>3: drink water</div> <div>4: fry</div> <div>5: lie down</div> <div>6: flush</div> <div>7: go downstairs</div> <div>8: go upstairs</div> <div>9: sit</div> <div>10: stand</div> <div>11: manipulate door</div> <div>12: type</div> <div>13: jump</div> <div>14: run</div> <div>15: walk</div> <div>16: wash hands</div> <div>17: write</div> <div>18: operate light</div> <div>19: eat</div>
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.