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1,961 results for “Sensing”
Dataset for "Using Adaptive Immersive Environments to Stimulate Emotional Expression and Connection in Dementia Care: Insights from User Perspectives towards SENSE-GARDEN"
<p>This dataset contains all qualitative interview data recorded from early stage research on an adaptive, immersive, multi-sensory intervention that is being developed for people living with dementia (SENSE-GARDEN). 52 semi-structured interviews were conducted with people living with mild cognitive impairment, informal caregivers, and professional caregivers across Belgium, Norway, Portugal, and Romania. The aim of these interviews was to collect initial user responses towards SENSE-GARDEN. </p> <p>The pdf file "Registration Sheet and Interview Questions" lists the questions that were asked during the interviews. The excel file "Interview Data with Thematic Analysis" contains all raw interview data with ideas, notes, and codes made during thematic analysis. The first three sheets in the file correspond to the user type (Person with mild cognitive impairment/Informal Caregiver/Professional Caregiver). The fourth sheet, "Overall themes", gives an overview of each theme, subtheme, and relevant quotes belonging to these themes. </p> <p>This research was conducted as part of a larger project. The SENSE-GARDEN project (AAL/Call2016/054-b/2017, implementation period June 2017 - May 2020) is funded by AAL Programme, co-funded by the European Commission and National Funding Authorities of Norway, Belgium, Romania, and Portugal. </p>
Distributed temperature sensing and associated data - Martha's Vineyard Coastal Observatory 2014
<p>Distributed temperature sensing (DTS) and associated calibration data from deployment on the Martha's Vineyard inner shelf during summer 2014.</p> <p>Each zip file contains a readme.txt file describing the contents in detail. Further information on the study is detailed in:</p> <ul> <li>Connolly, T. P. and A. R. Kirincich (2019) High-resolution observations of subsurface fronts and alongshore bottom temperature variability over the inner shelf, Journal of Geophysical Research. doi:<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018JC014454">10.1029/2018jc014454</a></li> </ul> <p><strong>DTS_MVCO_xml.zip</strong> - original XML files created by the DTS instrument, one file per trace (~50 GB uncompressed)</p> <p><strong>DTS_MVCO_cal.zip</strong> - original text files containing temperature data used for calibrating the DTS instrument, as well as information on positions and timing from the DTS deployment</p> <p><strong>DTS_MVCO_nc.zip</strong> - processed DTS data and associated calibration data in NetCDF format</p>
Datasets associated with: Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Global Ecology and Biogeography
<p>Data associated with the paper 'Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Global Ecology and Biogeography' by Lembrechts JJ et al., published in Global Ecology and Biogeography.</p> <p>Contains a dataset containing all extracted and measured temperature variables for all 106 measurement plots (climatedata), as well as the climate and species data used in the Species Distribution Models (SDMs). </p> <p>For details on the content of the table, see the readme-file, for details on methodology, see the original paper. </p>
Development, Training, Education, and Implementation of Low-Cost Sensing Technologies for Bridge Structural Health Monitoring (SHM)
<p>Corresponding data set for Tran-SET Project No. 17STUNM02. Abstract of the final report is stated below for reference:</p> <p>"Transportation infrastructure needs continuous monitoring. However, traditional inspections cost money and are conducted visually. New technologies for bridge monitoring are expensive and complex. This project involved developing cost-effective sensor technologies that can be applied towards the maintenance of railroad bridges by recording reference-free transverse displacement. More specifically, this project developed new applications of new technologies (Arduino, wireless smart sensors, drones, Hololens) and promoted workforce development with an emphasis on outreach of high-school students. This project was carried out in three main phases: (1) development and validation of technologies, (2) education and outreach to students, and (3) outreach to industry consisting in one professional workshop. The findings from the first phase showed that the data gathered by these new low-cost sensing systems were comparable to the data collected using traditional sensors. Researchers collected the findings of the second phase of the project through surveys conducted from Middle school, High school and college students during and after outreach activities. these surveys showed that many of the participant students got more interested in the use of new technologies after getting familiar with them. Finally, researchers collected the findings of the third phase of the project through a workshop collecting the interest and challenges of the owners of railroad infrastructure. The top interest of railroad owners is to explore the use of new technologies to increase safety in the field. The conclusions of this research include prioritization on developing low-cost technologies that can measure simple parameters in the field of interest to existing inspectors."</p>
Training Data for "Making sense of a newly assembled genome"
<p>The data provided here is part of the Galaxy Training Network tutorial for Making sense of a newly assembled genome. This data was sourced from NCBI on 2019-08-29</p>
data set related to article Independent adaptation mechanisms for numerosity and size perception provide evidence against a common sense of magnitude
<p>This record contains raw data related to article Independent adaptation mechanisms for numerosity and size perception provide evidence against a common sense of magnitude</p>
Drought impacts on Australian vegetation during the Millennium Drought measured with multi-source spaceborne remote sensing
<p>During the period from 1997 to 2009, Australia experienced a severe and persistent drought known as the Millennium Drought (MD). Major water shortages were reported across the continent as were various field accounts of tree mortality and dieback, but large-area assessment has been lacking. Given uncertain projections of future drought conditions in South-East Australia, analysis of the MD presents a valuable opportunity to assess possible impacts of these future trends. In this study, we analyzed the <strong>magnitude and sensitivity of vegetation responses to the MD</strong> with satellite-derived information including the fraction of photosynthetically absorbed radiation (FPAR), photosynthetic vegetation cover (PVC), canopy density derived from vegetation optical depth (VOD) and aboveground biomass carbon (ABC). <strong>Bioclimatic diferences in drought impacts and sensitivity</strong> were examined as well. Here are generated datasets and related codes for processing. Drought impcats and sensitivities for FPAR and PVC are too large to be uploaded but users could calculate their own results with the codes provided here. See Metadata.txt for details.</p>
Occupancy Sensing and Activity Recognition with Cameras and Wireless Sensors
<p>This dataset contains human activity data from a wireless sensing system, which includes a Doppler motion sensor and a wireless network. The Doppler sensor is a low-cost dual Doppler sensor modified from a commercial-off-the-shelf range-controlled radar, which operates at 5.8 GHz with two directional antennas. The wireless network uses four IEEE 802.15.4 radio nodes (CC2531 from TI) to create a mesh network to measure the RSS between each pair of radio nodes operating on the 16 frequency channels at 2.4 GHz.</p> <p>For the activity experiment, we recruited human subjects to perform 42 trials of four activities (each one with two minutes duration): (1) walking in a room (10 trials), (2) sitting in a chair (10 trials), (3) lying on a bed (12 trials), and (4) body turning on a bed (10 trials). For the walking activity, the human subjects walk along different paths at different locations in the room. For the lying on bed activity, we ask human subjects to breathe normally on bed with three orientations facing upwards, right and left. Finally, for the turning on bed case, human subjects turn their bodies from one side to the other on bed with random time intervals. We also recorded two-minute data of the empty room case before and after each human subject trial. Note that each data file name has its corresponding activity in it, so it is pretty self-explanatory. </p>
Dataset: User side acquisition of People-Centric Sensing in the Internet-of-Things
<p>- This archive contains the files submitted to the 2nd International<br> Workshop on Data: Acquisition To Analysis (DATA) at SenSys. Files<br> provided in this package are associated with the paper titled<br> "Dataset: User side acquisition of People-Centric Sensing in the<br> Internet-of-Things"</p> <p>- Content of the package:</p> <p> + 1_beacon_table.pkl: The beacon table in Pickle format. It contains<br> 20612286 data points where each data point represents a Bluetooth<br> beacon with 15 attributes as follows: <_id, host_id, ble_address,<br> sound_avg_peak, sound_max_peak, sound_count_over_thres_per_frame,<br> sound_avg_all, sound_avg_over_thres, temperature, humidity,<br> pressure, eco2_ppm, tvoc_ppb, rssi, timestamp>.</p> <p> + 2_device_description_table.pkl: The device description table<br> provides the mapping between a device's Bluetooth address and its<br> physical identity (device_id, description, type).</p> <p> + 3_checkin_table.pkl: The check-in table provides a timeseries of<br> user interactions with three Android tablets (i.e. tuples of <time,<br> host_id, checkpoint device>).</p> <p> + 4_sample_beacon_table.pkl: The sample beacon table in Pickle<br> format. It contains 1000 data points where each data point<br> represents a Bluetooth beacon with 15 attributes as follows: <_id,<br> host_id, ble_address, sound_avg_peak, sound_max_peak,<br> sound_count_over_thres_per_frame, sound_avg_all,<br> sound_avg_over_thres, temperature, humidity, pressure, eco2_ppm,<br> tvoc_ppb, rssi, timestamp>.</p> <p> + 5_sample_device_description_table.pkl: The sample device description<br> table provides the mapping between a device's Bluetooth address and<br> its physical identity (device_id, description, type).</p> <p> + 6_sample_checkin_table.pkl: The check-in table provides a<br> timeseries of user interactions with three Android tablets<br> (i.e. tuples of <time, host_id, checkpoint device>).</p> <p> + print_table_heads.py: A Python script which fetches Pickle tables<br> as DataFrames and prints out the sample entries.</p> <p><br> - ACM Reference Format: Chenguang Liu, Jie Hua, Tomasz Kalbarczyk,<br> Sangsu Lee, and Christine Julien. 2019. Dataset: User side<br> acquisition of People-Centric Sensing in the Internet-of-Things. In<br> The 2nd Workshop on Data Acquisition To Analysis(DATA’19), November<br> 10, 2019, New York, NY, USA. ACM, New York, NY, USA, 3 pages.<br> https://doi.org/10.1145/3359427.3361914</p>
Muti-type Aircraft of Remote Sensing Images: MTARSI
<p>MTARSI has a total of 9'385 remote sensing images acquired from Google Earth satellite imagery and manually expanded, including 20~different types of aircraft covering 36~airports.<br> The new data set is made up of the following 20 aircraft types: B-1, B-2, B-29, B-52, Boeing, C-130, C-135, C-17, C-5, E-3, F-16, F-22, KC-10, C-21, U-2, A-10, A-26, P-63, T-6, T-43.<br> All the sample images are carefully labeled by seven specialists in the field of remote sensing images interpretation. <br> Each image contains one and only one complete aircraft.</p>
Development of a global 30-m impervious surface map using multi-source and multi-temporal remote sensing datasets with the Google Earth Engine platform
<p>An accurate global impervious surface map at a resolution of 30-m for 2015 by combining Landsat-8 OLI optical images, Sentinel-1 SAR images and VIIRS NTL images based on the Google Earth Engine (GEE) platform.</p>
Fig. 17.5 in Chapter 17: Gigantism, Dwarfism, and Cope's Rule: "Nothing in Evolution Makes Sense without a Phylogeny"
Fig. 17.5. Three examples of bodysize evo
Fig. 1 in The sixth sense in mammalian forerunners: Variability of the parietal foramen and the evolution of the pineal eye in South African Permo-Triassic eutheriodont therapsids
Fig. 1. Measurement protocol of foramen magnum, parietal foramen, and orbit in Therapsida.
Data From: BioSense: An Automated Sensing Node for Organismal and Environmental Biology
<p><strong>Article title</strong></p> <p>BioSense: An Automated Sensing Node for Organismal and Environmental Biology</p> <p> </p> <p><strong>Authors</strong></p> <p>Andrea Contina<sup>a,b</sup>, Eric Abelson<sup>b</sup>, Brendan Allison<sup>b</sup>, Brian Stokes<sup>b</sup>, Kenedy F. Sanchez<sup>c</sup>, Henry M.Hernandez<sup>d</sup>, Anna M. Kepple<sup>b</sup>, Quynhmai Tran<sup>b</sup>, Isabella Kazen<sup>d</sup>, Katherine A. Brown<sup>e,f</sup>, Je'aime H. Powell<sup>g</sup>, Timothy H. Keitt<sup>b</sup></p> <p> </p> <p><strong>Affiliations</strong></p> <p><span>a Permanent address: School of Integrative Biological and Chemical Sciences, The University of Texas Rio Grande Valley, Brownsville, TX 78520, USA. </span></p> <p><span>b Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78703, USA. </span></p> <p><span>c Carnegie Mellon University, Pittsburgh, PA 15213, USA.</span></p> <p><span>d Department of Physics, The University of Texas at Austin, Austin, TX 78712, USA. </span></p> <p><span>e The Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA.</span></p> <p><span>f Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, UK</span></p> <p><span>g Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78758, USA.</span></p> <p> </p> <p><strong>Corresponding author’s email address <br></strong></p> <p><em>Timothy Keitt, </em><a href="mailto:tkeitt@utexas.edu"><em>tkeitt@utexas.edu</em></a><em>. <br></em></p> <p> </p> <p><strong>Abstract </strong></p> <p>Automated remote sensing has revolutionized the fields of wildlife ecology and environmental science. Yet, a cost-effective and flexible approach for large scale monitoring has not been fully developed, resulting in a limited collection of high-resolution data. Here, we describe BioSense, a low-cost and fully programmable automated sensing platform for applications in bioacoustics and environmental studies. Our design offers customization and flexibility to address a broad array of research goals and field conditions. Each BioSense is programmed through an integrated Raspberry Pi computer board and designed to collect and analyze avian vocalizations while simultaneously collecting temperature, humidity, and soil moisture data. We illustrate the different steps involved in manufacturing this sensor including hardware and software design and present the results of our laboratory and field testing in southwestern United States.</p>
Data/Code for: Sediment dynamics in the energetic nearshore zone: Acoustic remote sensing and model validation
<p>This archive contains data and postprocessed results used in the article "Sediment dynamics in the energetic nearshore zone: Acoustic remote sensing and model validation" by G. Wilson, P. Dickhudt & J. Aldrich. All data and code in this archive is copyright of the authors. Please contact the authors prior to publishing new results or derivative works based on data/code from this archive.</p>
Glutamine sensing licenses cholesterol synthesis.
<p>The uploaded metabolomic dataset contains liquid-chromatography-mass spectrometry (LC-MS) data associated to a publication of Bruna Martins Garcia et al. from the Lena Pernas laboratory.</p> <p>The title olf the article is: <strong>Glutamine sensing licenses cholesterol synthesis.</strong></p> <p>This article is to be published 2024 in the EMBO Journal-</p> <p>Abstract of the article: The mevalonate pathway produces essential metabolites such as cholesterol. Although this pathway is negatively regulated by metabolic intermediates, little is known of the metabolites that positively regulate its activity.<em> </em>We found that the amino acid glutamine is required to activate the mevalonate pathway. Glutamine starvation inhibited cholesterol synthesis and blocked transcription of the mevalonate pathway—even in the presence of glutamine derivatives such as ammonia and a-ketoglutarate. We pinpointed this glutamine-dependent effect to a loss in the ER-to-Golgi trafficking of SCAP that licenses the activation of SREBP2, the major transcriptional regulator of cholesterol synthesis. Both enforced Golgi-to-ER retro-translocation and the expression of a nuclear SREBP2 rescued mevalonate pathway activity during glutamine starvation. In a cell model of impaired mitochondrial respiration in which glutamine uptake is enhanced, SREBP2 activation and cellular cholesterol were increased. Thus, the mevalonate pathway senses and is activated by glutamine at a previously uncharacterized step, and the modulation of glutamine synthesis may be a strategy to regulate cholesterol levels in pathophysiological conditions. </p> <p>The associated data in this repository is grouped according to the figures in the the above mentioned article. Each zip folder contains the LC-MS raw files and one or more Excel tables describing the parameters (retention time, observed molecular weight, detected error to expected molecular weight, signal-to-noise and the integrated raw values of the detected compounds. Material and Method utilized for the analysis of the diverse samples is available in the context of the above mentioned article.</p>
DCP-MTL: Vectorization of Agricultural Cultivation Field Parcels via Boundary-Parcel Multi-Task Learning Network in Ultra-High-Resolution Remote Sensing Images
<p><span>This paper introduces the first UHR UAV dataset specifically for CFP, designed to evaluate the performance of the proposed model in identifying these parcels. </span><span>The dataset offers ultra-high spatial resolution, various field parcel types, and broad geographic coverage. </span><span>Figure 8 </span><span>shows </span><span>the spatial distribution of the study data. Jilin Province is the primary region for training and evaluating the model, while Hebei, Henan, Anhui, Zhejiang, and Hainan are auxiliary regions for testing the model's transferability. </span></p>
data_set for "Time-resolved sensing of electromagnetic fields with single-electron interferometry"
Open the record for dataset details and reuse information.
Data-driven Soil Moisture Sensing with mmWave Radar In Environment 1
<p>This is a millimeter-wave radar soil moisture dataset, which includes soil samples with 20 different moisture levels, ranging from 6.20% to 43.82% with approximately 2% intervals. In the bin file names, the first number after "data" represents the soil moisture level, labeled as 1 to 20, with moisture levels of 6.20%, 8.30%, 10.80%, 12.60%, 14.44%, 16.14%, 18.96%, 19.70%, 23.34%, 25.24%, 26.24%, 28.90%, 31.10%, 32.46%, 33.80%, 36.44%, 38.20%, 40.56%, 42.68%, and 43.82%, respectively.In the bin file names, the second number after "data" represents the height of the radar development board above the soil surface, measured in centimeters, specifically 16 cm, 20 cm, and 24 cm. The data was collected using the IWR1843 radar board, with 1 transmitting antenna and 4 receiving antennas. Each bin file contains 128 radar frames, with each frame consisting of 32 chirps, and each chirp containing 384 sampling points.</p>
Mapping Soil Organic Carbon in the World's Largest Arid Mangrove Forest (Indus Delta, Pakistan): A Multi-Sensor Remote Sensing and Machine Learning Approach
<p>Mangrove forests play a crucial role in carbon sequestration, especially in arid regions where their ability to store carbon in soil is vital for mitigating climate change. The Indus Delta in Pakistan, the world’s largest arid mangrove forest system, lacks spatially explicit data on Soil Organic Carbon (SOC) despite its importance for conservation and carbon budgeting. This study aims to establish a baseline SOC map 2020 at 10 m spatial resolution using Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (MultiSpectral Instrument) satellite imagery, integrated with in-situ soil sampling. SOC predictions were made using a Classification and Regression Tree (CART) machine learning model within the Google Earth Engine platform, leveraging 40 predictor variables, including spectral bands and derived indices. A total of 53 topsoil (0-10 cm) samples were collected in February 2020 across the Indus Delta, and SOC was analyzed using the Walkley-Black method. The results showed an average SOC value of 65.88 Mg C ha⁻¹ with substantial spatial variability, ranging from 15.06 Mg C ha⁻¹ to 138.03 Mg C ha⁻¹ with a total of 0.91 Pg C. The CART model demonstrated high accuracy, with an R² of 0.95 and an RMSE of 9.18 Mg C ha⁻¹. However, the region faces challenges such as seawater intrusion and salinity, which threaten its ability to sequester carbon. With the first high-resolution SOC map for the Indus Delta, this study provides valuable insights for ecosystem management, conservation planning, and carbon budgeting. These findings of this study have the potential to significantly influence initiatives like REDD+ and Blue Carbon projects, which aim to enhance carbon sequestration while addressing the ecological challenges facing Pakistan’s mangroves</p>
ScienceDex guides
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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.