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8,460 results for “MONITORING”
Continuous monitoring of patient mobility for 18 months using inertial sensors following traumatic knee injury: a case study
<p><strong>This repository contains raw data relating to: </strong>Continuous monitoring of patient mobility for 18 months using inertial sensors following traumatic knee injury: a case study Mueller A., Hoefling H., Nuritdinow T., et al. DOI: 10.1159/000490919</p> <p><strong>Metadata and processed data derived from the raw data deposited here is available here:</strong> https://github.com/Novartis/mueller_et_al_2018</p> <p><strong>Article Abstract</strong></p> <p>Continuous patient activity monitoring during rehabilitation, enabled by digital technologies, will allow the objective capture of real-world mobility and aligning treatment to each individual’s recovery trajectory in real time. To explore the feasibility and added value of such approaches, we present a case study of a 36-year-old male participant monitored continuously for activity levels and gait parameters using a waist-worn inertial sensor following a tibial plateau fracture on the right side, sustained as a result of a high-energy trauma during a sporting accident. During rehabilitation, data were collected for a period of 553 days, with > 80% daytime compliance, until the participant returned to near full mobility. The participant completed a daily diary with the annotation of major events (falls, near falls, cycling periods, or physiotherapy sessions) and key dates in the patient’s recovery, including medical interventions, transitioning off crutches, and returning to work. We demonstrate the feasibility of collecting, storing, and mining of continuous digital mobility data and show that such data can detect changes in mobility and provide insights into long-term rehabilitation. We make both raw data and annotations available as a resource with the aspiration that further methods and insights will be built on this initial exploration of added value and continue to demonstrate that continuous monitoring can be deployed to aid rehabilitation.</p>
Long-term moss monitoring network for atmospheric deposition in Germany, link to research data and scientific software
<p>Research data and scientific software related to a study that aims to restructure a long-term monitoring network using moss as biomonitor for atmospheric deposition in Germany. Data from the European Moss Survey 2005 and a statistically based methodology including a decision support system were used to design the spatial network for the 2005 survey.</p>
Umstrukturierung des deutschen Moos-Monitoring-Messnetzes 2015, Link zu Forschungsdaten und wissenschaftlicher Software
<p>Forschungsdaten und wissenschaftliche Software zu einer Studie, welche der Reduzierung des räumlichen Messnetzes des deutschen Moos Monitorings 2005 (728 Standorte) für das Moos Monitoring 2015 (402 Standorte) diente. Die Studie fußt auf einer statistisch begründeten Methodik, welche kombiniert mit einem Entscheidungsmodell für die Standortauswahl 2015 auf Daten des Moos Monitorings 2005 angewendet wurde.</p>
Exergame for Continuous and Transparent Monitoring of Handgrip Strength and Endurance
<p>Grip Strength reduction is a marker of the age-related functional decline. An anti-stress ball and an exergame to continuously and transparently monitor handgrip strength and endurance in older persons have been developed within the context of the European Project MOVECARE. The description of the exergame can be found in the paper associated to the dataset here uploaded. </p> <p>The game was tested on ten young healthy subjects and the results of maximal handgrip force provided by the game were compared to the handgrip measured with the Jamar dynamometer. High correlation was found, suggesting that the game can represent a promising and non-intrusive tool to help detecting early signs of frailty in community-dwelling older persons. </p> <p>The data here uploaded consist of an excel file with two sheets. </p> <p>- sheet 1: Values of handgrip measured by the Jamar and the anti-stress ball on 10 subjects. Each subject performed three repetitions of each measurement. </p> <p> - sheet 2: the System Usability Scale scores collected on the 10 subjects. Scores of individual questions, as well as total scores, are reported. </p>
Testing Smart City environmental monitoring technology using small scale temporary cities
<p>This is the data used for:</p> <blockquote> <p>S. J. Johnston <em>et al</em>., "Testing Smart City environmental monitoring technology using small scale temporary cities," <em>2019 IEEE 5th World Forum on Internet of Things (WF-IoT)</em>, 2019, pp. 578-583, doi: 10.1109/WF-IoT.2019.8767274.</p> </blockquote> <p> </p> <p><em><strong>Abstract:</strong></em></p> <p>Exposure to particulate matter has been identified as a major health problem worldwide. Established measurement<br> techniques require equipment costing many thousands of dollars and specialist expertise to maintain. Ongoing research<br> is investigating the use of low cost <$300 sensors to enable greater temporal-spatial density of readings to be taken. There<br> are questions about the suitability and reliability of these low-cost sensors, queries which can be addressed by deploying<br> and evaluating the sensors in a real world application. We propose festival site as small scale cities to enable a short term<br> deployments and evaluation of sensors. We present data from these devices and experiences gained from using a festival site as a substitute for a city.</p> <p><em><strong>Files:</strong></em></p> <p>1 - timeLapse: mp4 file presenting a time lapse of the measurements realised during the festival<br> 2 - sensor_data: csv file containing the 5 min averaged data from all the sensors deployed and their coordinates used to generate the graph and the maps in the paper</p> <p>3 - workshop.pdf: instructions to run the workshop and code used for the workshop</p>
EOSCpilot D3.7 Updates to Policy Supporting Services: supporting material for the Monitor
<p>This is to support use cases of the Monitor section of EOSCpilot D3.7 Updates to Policy Supporting Services.</p>
Monitoring of the Vistula estuary into the Baltic Sea using Sentinel-2 data
<p>Senitnel-2 data and dedicated presentations were used during the Daily Animation. The aim of the exercise was to familiarize the participants with the structure of Senitnel-2 data and creating RGB compositions in SNAP and QGIS software.</p> <p>Links to the presentation:</p> <p>http://fabspace.pl/wp-content/uploads/2017/11/RGB-w-QGIS.pdf</p> <p>http://fabspace.pl/wp-content/uploads/2017/11/RGB-w-SNAP.pdf</p> <p> </p>
S16 | FRENCHLIST | French Monitoring List
<p>This is the collection associated with list S16 FRENCHLIST on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S16</p> <p>FRENCHLIST</p> <p><strong>French Monitoring List</strong></p> <p>French List <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/French_List_08052017.csv">CSV</a> (8/05/2017)</p> <p>CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/frenchlist">French Monitoring List</a></p> <p>Further curation in progress…</p> <p><a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/FrenchList_UniqueInChIKeys_08052017.txt">French List Unique InChIKeys</a> (8/05/2017)</p> <p>Provided by Valeria Dulio, curated by Reza Aalizadeh, University of Athens.</p>
Titration_DB: NMR-monitored protein pH titration chemical shift data.
<p>This archive contains a backup of the NMR titration_db database raw chemical shift data.<br> Reference: D. Farrell et al., “Titration_DB: Storage and analysis of NMR-monitored protein pH titration curves.,” Proteins, vol. 78, no. 4, pp. 843–857, 2009.</p> <p>This data was formerly hosted on an interactive website which is no longer active. The files are stored as csv format, one per protein. They are also split into three folders according to the nuclei whose chemical shifts were used to detect the measurement (1H,13C or 15 N). Much of this data was obtained from literature. A table in the zip file (titration_dataset_info.csv) associates the protein name with the original paper. </p> <p>Note that the database stored the pka values fit using a model to each residue dataset. This is not stored here.</p> <p>Also present is a file titdb.fs. This is a binary file that can only be opened using the PEATDB software. PEATDB (Protein Engineering Analysis Tool - Database) was an application for sharing, analysing and storing experimental and theoretical data from Protein Engineering experiments.<br> This is now legacy software and no longer maintained. It was used to allow interactive fitting of the titration data using the Ekin module.<br> It is still possible to install this software if you really need to. It is stored on github at https://github.com/dmnfarrell/peat.</p> <p>D. Farrell May 2019</p>
Collection of UV/Vis spectra acquired while monitoring reaction progress of thymidine phosphorolysis with varying reactant concentrations
<p>This data set accompanies the publication "Dynamic modelling of phosphorolytic cleavage catalyzed by pyrimidine-nucleoside phosphorylase" in MDPI Processes, and is a collection of UV/vis spectra used for monitoring of reaction progress under various experimental conditions.</p> <p>The monitored reaction is thymidine phosphorolysis, catalyzed by an enzyme (EC 2.4.2.2). Phosphate and thymidine concentrations are in the range of 2–80 mM and 0.8–5 mM, respectively, and final enzyme concentration in the range of 12.5–50 µg/mL. The recorded data represents time courses over 24 hours for 48 reaction conditions.</p> <p>This collection also contains the documentation of all work done in the laboratory to achieve this data (provenance in the form of the complete lab journal).</p>
Project STORM: monitoring the masonry of Hall I, at the Baths of Diocletian, with a prototype based on Arduino UNO. Dataset 2017 - 2019
<p>This dataset for monitoring the masonry of Aula I at the Baths of Diocletian (Rome) was created by the University of Tuscia.<br> The measurements are carried out with a prototype based on Arduino UNO, have been investigated:</p> <ul> <li>Temperature and Relative Humidity of the internal environment and Temperature and Relative Humidity of contact on the masonry (with two sensors DHT22 AM2302);</li> <li>Acceleration along the three spatial axes (X, Y and Z), Pitch Index and Roll Index (with Gy 521 sensor);</li> <li>Surface pressure for the movement of a lesion (with FlexiForxe sensor).</li> </ul> <p>The data produced by the sensors were acquired and sent to the computer which automatically saved them every 10 seconds. The dataset is composed of the data obtained from 2017 to 2019 and separated by month in 11 sheets. In total about 40 million values were recorded, used to understand the slow hazard phenomena present on the monitored masonry.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
Project STORM: monitoring the masonry of Michelangelo's Cloister, at the Baths of Diocletian, with Fiber Bragg Grating (FBG) sensors. RAW Dataset 2018 - 2019
<p>This dataset for monitoring the masonry of Michelangelo's Cloister at the Baths of Diocletian (Rome) was created by the University of Tuscia.<br> The measurements are carried out with a Fiber Bragg Grating (FBG) sensors, have been investigated:</p> <ul> <li>Strain of lesions (sensors S0 and S3);</li> <li>Temperature of masonry (sensors S1, S2 and S8);</li> <li>Humidity of masonry (sensors S4, S5, S6 and S7).</li> </ul> <p>The data produced by the sensors were automatically saved them every 30 seconds. The dataset is composed of the data raw obtained from October 2018 to May 2019 and separated by month in 8 sheets. In total more than 2 million values were registered, used to understand the slow hazard phenomena present on the monitored masonry.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
STORM Project: monitoring environment conditions at Baths of Diocletian site (Rome, Italy). Dataset 2018 - 2019
<p>This dataset was created by the Engineering Ingegneria Informatica S.p.A. through a set of prototypes based on Libelium Waspmote for collecting the following parameters: </p> <ul> <li>Climate parameters (Temperature, Relative Humidity, Barometric Pressure, Luminosity, Wind direction/speed and Rainfull) using a Libelim PlugAndSense Agricolture Pro;</li> <li>Environmental Parameters (Monoxide Carbon, Oxigen, Air Polluction, Volatile Organic Compounds VOC, Carbon Dioxide, Nitric Dioxide , Hydrogen Sulfide, Sulfure Dioxide and Particle Matter PM 1, 2.5 and 10) using two nodes: PlugAndSense Smart Cities Pro and Waspmote with gases sensor board;</li> <li>Acoustic Noise Sensor and Vibrations with accelerometer, using a prototype based on Libelium Waspmote.</li> </ul> <p>The data produced by the sensors were acquired and sent to the Meshlium (mini-pc linux based) which automatically saved and sent to the STORM Platform. The dataset is composed of the data obtained from February 2018 to March 2019.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
Project STORM: monitoring the masonry of Hall I, at the Baths of Diocletian, with Fiber Bragg Grating (FBG) sensors. RAW Dataset 2017 - 2019
<p>This dataset for monitoring the masonry of Hall I at the Baths of Diocletian (Rome) was created by the University of Tuscia.<br> The measurements are carried out with a Fiber Bragg Grating (FBG) sensors, have been investigated:</p> <ul> <li>Strain of lesions (sensors S0, S2 and S3);</li> <li>Temperature of masonry (sensors S1, and S8);</li> <li>Humidity of masonry (sensors S4, S5, S6 and S7).</li> </ul> <p>The data produced by the sensors were automatically saved them every 30 seconds. The dataset is composed of the data raw obtained from October 2017 to May 2019 and separated by month in 14 sheets. In total more than 4 million values were registered, used to understand the slow hazard phenomena present on the monitored masonry.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
Computed Basic Statistics of Hydraulics and Discharge Measures at USGS River Monitoring Stations
<p>The shared table contains basic statistics (average, standard deviation, minimum, maximum, and coefficient of variation [%]) river channel hydraulics and discharge records of the 4472 USGS river monitoring stations. The required raw data are free to access by the USGS-<em>National Water Information System</em> (<a href="https://waterdata.usgs.gov/nwis">https://waterdata.usgs.gov/nwis</a>). Hydraulics and discharge records that measured at each USGS monitoring site, given a long time period, were assembled, assessed, and finally used for computing the basic statistics. </p>
WASH in households dataset (from the Joint Monitoring Programme database). June 2019
<p>This dataset compromises all country files included in the WHO/UNICEF Joint Monitoring Programme (JMP) global database (<a href="https://washdata.org/data/household">https://washdata.org/data/household</a>, downloaded June 2019).</p> <p>It includes:</p> <p><strong><em>Country</em>:</strong> ISO 3 code + Complete name</p> <p><strong><em>Service</em>:</strong> Water or Sanitation</p> <p><strong><em>Setting</em>: </strong>Urban or Rural</p> <p><strong><em>Source</em>: </strong>Category of the household survey</p> <p><strong><em>Year</em>: </strong>Date of the household survey</p> <p><strong><em>X<sub>1</sub>, X<sub>2</sub> and X<sub>3</sub>:</em></strong> percentage of the population using…</p> <ul> <li>In <strong>Water</strong>: X<sub>1</sub> all improved drinking water sources; X<sub>2</sub> piped drinking water sources and X<sub>3</sub> no drinking water facility (surface water).</li> <li>In <strong>Sanitation</strong>: X<sub>1</sub> all improved sanitation facilities; X<sub>2</sub> improved sanitation facilities connected to sewers and X<sub>3</sub> no sanitation facilities (open defecation).</li> </ul> <p>The dataset is used in the following paper:</p> <p><em>Ezbakhe, F. and Pérez-Foguet, A. (2019) Estimating access to drinking water and sanitation: The need to account for uncertainty in trend analysis. Science of the Total Environment. DOI: 10.1016/j.scitotenv.2019.133830</em></p> <p><a href="https://doi.org/10.1016/j.scitotenv.2019.133830">https://doi.org/10.1016/j.scitotenv.2019.133830</a></p>
Monitoring long-term peat subsidence with subsidence platens in Zegveld, The Netherlands
<p><span>Peat oxidation in peat meadow areas is causing greenhouse gas emissions as well as land subsidence. Due to yearly fluctuations in soil surface level, long-term monitoring is needed to determine long-term net subsidence rates. In the experimental peat-meadow farm at Zegveld (NL) subsidence platens were installed in 1970 in a field with low ditchwater level, and in 1973 in a field with high ditchwater level. Platens were installed at 7 different depths, allowing to investigate where in the peat profile subsidence occurs. Elevation of platens as well as soil surface has been measured with surveyor’s levelling each year at the end of winter, so that a long timeseries up to 2023 is available. Analysis showed that surface level in the field with high ditchwater level subsided by 23 cm in 50 years (4.6 mm/yr), while in the field with low ditchwater level this was 31 cm in 53 years (5.8 mm/yr). Results also showed that in the field with low ditch water level, most subsidence due to permanent shrinkage and peat oxidation occurred between 40 and 100 cm depth, while for the other field this was between 20 and 40 cm depth. Finally, in 2023 subsidence was still observed under continuously saturated conditions at 140 cm depth. Presumably, in the aerated part of the profile peat oxidation and the associated earthification process is the main cause of subsidence, while the observed subsidence in the saturated soil at 140 cm depth must be due to other processes, such as consolidation and creep.</span></p>
Groundwater monitoring with an underwater DAS1 cable, processed dataset
<p>This repository contains data to reproduce figures from the article "Groundwater monitoring with an underwater DAS cable " by Destin Nziengui Bâ, Aurélien Mordret, Olivier Coutant, Camille Jestin and Ludovic Bodet </p>
Synthetic Dutch Homicide Monitor
<p>This is the synthetic version of the Dutch Homicide Monitor, a database registering case-, victim-, and perpetrator information of all homicides committed since 1992.</p> <p>This synthetic version is based on 10 years of homicide data. The code for the synthetic data generation is available on Github (https://github.com/KKrusselmann/SENSYN). More information on the original Dutch Homicide Monitor, including a detailed code book is available on Leiden Universitys website (https://www.universiteitleiden.nl/en/research/research-projects/governance-and-global-affairs/european-homicide-monitor#tab-1).</p>
Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values - Dataset
<p>This dataset accompanies the report <em>"Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values"</em>, which is available via Zenodo.<br><br>It provides record-level data of UKRI-funded and UK-affiliated research output (limited to journal articles with Crossref DOIs) published between 2012 and 2022 - including bibliographic metadata as well as data on open access availability, publisher, national and international collaborations, citations, views and downloads, altmetrics and subjects (fields). All variables are documented in the data dictionary included in this Zenodo record.</p> <p>The code used to generate the dataset from open data sources is available on GitHub. </p> <p>The following data sources were used:</p> <ul> <li> <p>Gateway to Research (records downloaded between 2023-11-05 and 2023-11-13)</p> </li> <li> <p>Crossref (Metadata Plus snaphot 2023-10-31, Crossref member route API 2024-01-23)</p> </li> <li> <p>OpenAlex (data snapshot 2023-10-18)</p> </li> <li> <p>Unpaywall (data snapshot 2023-11-27)</p> </li> <li> <p>IRUS UK (2024-04-03)</p> </li> <li> <p>Crossref Event Data (2023-04-01)</p> </li> </ul> <p><strong></strong><br><br>The project made use of Curtin Open Knowledge Initiative (COKI) infrastructure, which is documented on GitHub: <a href="https://github.com/The-Academic-Observatory">https://github.com/The-Academic-Observatory</a>. </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.