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8,460 results for “MONITORING”
Swiss Job Market Monitor - ICT term annotation
<p>Extracts of 2000 German-speaking job ads from 1990-2021, annotated on token-level with ICT terms, in IOB format.</p>
Apple 2 and Apple monitor 3
Apple 2 and Apple monitor 3 models Source: Objaverse 1.0 / Sketchfab
Figure 1 from: Leese F, Altermatt F, Bouchez A, Ekrem T, Hering D, Meissner K, Mergen P, Pawlowski J, Piggott J, Rimet F, Steinke D, Taberlet P, Weigand A, Abarenkov K, Beja P, Bervoets L, Björnsdóttir S, Boets P, Boggero A, Bones A, Borja Á, Bruce K, Bursić V, Carlsson J, Čiampor F, Čiamporová-Zatovičová Z, Coissac E, Costa F, Costache M, Creer S, Csabai Z, Deiner K, DelValls Á, Drakare S, Duarte S, Eleršek T, Fazi S, Fišer C, Flot J, Fonseca V, Fontaneto D, Grabowski M, Graf W, Guðbrandsson J, Hellström M, Hershkovitz Y, Hollingsworth P, Japoshvili B, Jones J, Kahlert M, Kalamujic Stroil B, Kasapidis P, Kelly M, Kelly-Quinn M, Keskin E, Kõljalg U, Ljubešić Z, Maček I, Mächler E, Mahon A, Marečková M, Mejdandzic M, Mircheva G, Montagna M, Moritz C, Mulk V, Naumoski A, Navodaru I, Padisák J, Pálsson S, Panksep K, Penev L, Petrusek A, Pfannkuchen M, Primmer C, Rinkevich B, Rotter A, Schmidt-Kloiber A, Segurado P, Speksnijder A, Stoev P, Strand M, Šulčius S, Sundberg P, Traugott M, Tsigenopoulos C, Turon X, Valentini A, van der Hoorn B, Várbíró G, Vasquez Hadjilyra M, Viguri J, Vitonytė I, Vogler A, Vrålstad T, Wägele W, Wenne R, Winding A, Woodward G, Zegura B, Zimmermann J (2016) DNAqua-Net: Developing new genetic tools for bioassessment and monitoring of aquatic ecosystems in Europe. Research Ideas and Outcomes 2: e11321. https://doi.org/10.3897/rio.2.e11321
Figure 1 - GANTT Diagram showing the planned COST Networking Tools to be applied to DNAqua-Net.
Data used for "Effects of environmental change on population growth: monitoring time-varying carrying capacity in free-ranging spotted hyenas"
<p>These data are to be used within the context of the R code provided <a title="link to GitHub" href="https://github.com/hyenaproject/Bailey2024K" target="_blank" rel="noopener">here</a>. See the linked GitHub repository for details.</p>
Monitoring soil surface change under simulated rainfall using image-based 3D reconstruction.
<p>This dataset contains three-dimensional (3D) soil surface information from rainfall simulation experiments in laboratory and field conditions. </p> <ul> <li>3D data are provided as .img rasters contained in the zip file</li> <li>Information pertaining to experimental conditions and treatments are provided as tables in the csv files</li> <li>Table columns are defined in the 3D_Data_Description.pdf file</li> </ul> <p>Notes: Treatment abbreviations in the laboratory experiment are Seep for seepage condition and Drain for drainage condition (See <a href="https://doi.org/10.2136/sssaj2012.0013">doi.org/10.2136/sssaj2012.0013</a>).</p>
MONITOR Questionnaire and Response Dataset
<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. The project was funded by <strong>Stiftelsen för internationalisering av högre utbildning och forskning (STINT) </strong>under <strong>Grant Agreement No. 202100-2916</strong>.</p> <p>This project has unfolded many benefits for both partnering universities. It gave access to a wide variety of data regarding mental health-related parameters. 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. As a partner of this project, NTG provided the necessary manpower and data collection environment for the project. </p> <p> </p> <h3><strong>Test Subject Demography</strong></h3> <p>A total of <strong>27</strong> subjects <strong>(29.17% Male, 83.33% Female, Age Group: 18-40 years) </strong>were recruited in a random fashion from different job role with written consent to collect stress-related physiological parameters for this project. 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. </p> <h3><strong>Data Collection Unit</strong></h3> <p>Two data collection devices were used simultaneously on the test subject. One of them is <a href="https://shimmersensing.com/product/shimmer3-gsr-unit/" target="_blank" rel="noopener">Shimmer</a> Sensor which is ISO certified for physiological data collection. The second data collection unit is <strong>PhysiCube</strong> which is assembled by the research team of MONITOR project as a low-cost solution. The dataset collected using Shimmer Sensor is available in <a href="../records/11523283" target="_blank" rel="noopener">https://zenodo.org/records/11523283</a> and the dataset collected using <strong>PhysiCube </strong>is available in <a href="../records/11518751" target="_blank" rel="noopener">https://zenodo.org/records/11518751.</a></p> <h3><strong>Data Collection Sessions</strong></h3> <p>Data were collected in the following three phases-</p> <ol> <li><strong>Baseline Session:</strong> The duration of a baseline session is 18~22 mins and took place in the morning in between normal working hour (Before Lunch). The physiological values like Heart rate, GSR etc. can vary time to time for different reasons even there is no event of stress. This session establishes the base line value of these parameter for individual subjects. During this time, test subject responds to a questionnair with an aim to establish the base stress level. </li> <li><strong>After-Lunch Session: </strong>After-lunch session took place on the same day of Baseline session for an individual test subject with the assumption that a break in work allow a worker to relax. Test subjects were asked to relax and do some breathing exercise during this session.</li> <li><strong>Stress-Induced Session: </strong>This session took place on a different date. In this session the test subjects had to answer general knowledge and mathematical questions which people often get wrong, and did so under time pressure to induce stress [1]. After the session, test subjects were asked about the level of stress that they have felt during this session which indicates perceived stress level. </li> </ol> <h3><strong>Dataset Description</strong></h3> <p><strong>_test subject info.csv : </strong>This file contains the generic information about the test subject which may have relations with indivdual stress level. The informations are age group, gender, marital status, job role, approximate year of experience in current job role and approximate year of experience in Ready Made Garment (RMG) sector. To remove Personal Identifiable Information (PII), the data is generalized (e.g. age group), summarized (e.g. instead fo exact designation job role is used). Here operations indicate sewing operator, cutter, pressing operator etc.; whereas Supervisor indicates line supervisor, qulaity control supervisor, floor manager, production manager etc. </p> <p><strong>_baseline question.csv :</strong> This file contains the questionnair which was administered during the baseline session. There are 5 categories and each category has different set of questions.</p> <p><strong>_question option value.csv : </strong>Each question was answered with one of the five options and their assigned value can be found in this file.</p> <p><strong>_baseline score.csv : </strong>This file contains the response to each question. Each question is marked with CiQj. Here Ci meanse ith category and Qj means jth question of that category.</p> <p><strong>_stress induced score.csv: </strong>This file contains the score of perceived stress level in a scale of 1-10. Test subject were asked to score their own individual stress after the stress induced session.</p> <p> </p> <p> </p> <p>Reference:</p> <p>1. Almazrouei MA, Morgan RM, Dror IE. A method to induce stress in human subjects in online research environments. Behav Res Methods. 2023 Aug;55(5):2575-2582. doi: 10.3758/s13428-022-01915-3. Epub 2022 Jul 25. PMID: 35879504; PMCID: PMC9311341.</p>
Quantitative Study of Microplastic Degradation in Urban Hydrosystems: In-Situ Monitoring vs. Controlled Conditions using Accelerated Photo-oxidation
Open the record for dataset details and reuse information.
Use of genetics in monitoring control measures for Aedes aegypti and Aedes albopictus
<p>This study evaluated the impact of long-term pyriproxyfen exposure on the genetic structure and diversity of Ae. aegypti and Ae. albopictus mosquito populations. The study was conducted in Manaus, Amazonas, Brazil, where pyriproxyfen dissemination stations have been monitored since 2014 up to the present day. Double digest restriction-site associated DNA sequencing (ddRADSeq) was performed. Sequencing quality control was applied with mean quality filtering, trimming, minimum length filtering and adapter removal procedures using Trimmomatic. All sequencing reads were aligned with BWA program to the reference genome of Ae. aegypti (NC_035159.1) and Ae. albopictus (JAFDOQ000000000.1). Variant calling was performed using Stacks v2.62, with gstacks approach. The variant panel was exported in the VCF v4.2 format. SNPs were removed from the variant panel based on a minimum allele frequency (MAF) < 0.1 and at least 8 non-missing genotypes within each group.</p>
FIGURE 2 in Recommendations For Monitoring Avian Populations With Point Counts: A Case Study In Southeastern Brazil
FIGURE 2: Monthly mean climate measurements at Caetetus Ecological Station (T: temperature and R: rainfall) and monthly total counts of species richness and numbers of individuals in a semideciduous forest remnant in São Paulo, southeastern Brazil. Black circles represent number of species and white circles represent number of individuals.
FIGURE 3 in Recommendations For Monitoring Avian Populations With Point Counts: A Case Study In Southeastern Brazil
FIGURE 3: Comparison of morning and afternoon counts for the number of accumulated species by total number of individuals counted (top) and month (bottom). Months are separated by dry and rainy seasons.
FIGURE 1 in Recommendations For Monitoring Avian Populations With Point Counts: A Case Study In Southeastern Brazil
FIGURE 1: Location of the Caetetus Ecological Station (EECa) within the municipalities of Gália and Alvinlândia in the state of São Paulo, southeastern Brazil. Darker shades of grey indicate remnant natural vegetation.
Fig. 2 in Using dragonflies to monitor and prioritize lotic systems: a South African perspective
Fig. 2 Plot of Dragonfly Biotic Index (DBI) scores as a function of DBI/Site. Habitat condition categories (HH–LL) are explained in Table 1; DBI and DBI/Site scores are given in Table 2.
Gas chromatographic monitoring of the operation of a photoelectrochemical water splitting reactor
<p>Presented is a movie which shows the reading of a gas chromatograph Shimadzu GC 2014 during the photoelectrochemical water splitting of a PEC reactor under illumination with a solar simulator, assisted by an external DC bias providded by a Potentiostat Voltalab 80. </p>
Data from: Using acoustic monitoring to assess insectivorous bat richness and activity in a sub-tropical savanna
<p><span>Understanding insectivorous bat diversity and activity is crucial for conservation efforts, particularly in under-researched regions like sub-tropical savannas. This study aimed to assess bat species richness and seasonal activity in the MalaMala Game Reserve, located in the southern region of the Greater Kruger National Park (KNP), South Africa. We conducted acoustic monitoring using ultrasonic detectors over two distinct seasons: the wet season (January to March) and the dry season (August) in 2022. Species identification was performed using Kaleidoscope software, complemented by manual verification to minimize misidentifications, particularly for species with overlapping echolocation characteristics. Our findings revealed 16 species across six families, representing 40% of the bat species known in KNP. The Molossidae family was the most dominant, followed by Vespertilionidae, while the Hipposideridae family recorded the fewest calls, likely due to their high-frequency echolocation calls that attenuate rapidly. Notably, our findings aligned with previous research indicating year-round presence of insectivorous bats in savanna woodlands. Seasonal variations in bat activity were observed, with significantly higher activity during the wet season, likely due to increased insect abundance and reduced thermoregulatory costs. Our study provides a critical benchmark for future bat research in the Greater KNP landscape, highlighting the importance of continued monitoring to detect changes in bat populations and inform conservation strategies.</span></p>
Figure 2. BEAST chronogram from a in Molecular data extend Australian Cricotopus midge (Chironomidae) species diversity, and provide a phylogenetic hypothesis for biogeography and freshwater monitoring
Figure 2. BEAST chronogram from a data set of single representatives per species/clade that corresponds with Table 1. Lettered nodes are those for which time to most recent common ancestor (tmrca) was estimated and correspond with Table 1; black stars indicate nodes to which prior calibrations were applied. The time scale is in millions of years before present.
Severe L-band scintillation over low-to-mid latitudes caused by an extreme equatorial plasma bubble: joint observations from ground-based monitors and GOLD
<p>Data from ScintPis 3.0 that was used in the manuscript.</p>
Fig. 5 in Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring
Fig. 5.—Temporal occurrence pattern of the Antarctic blue whale song at each of the five hydrophones during the seasonal periods when the song was recorded in the South Taranaki Bight in New Zealand during 2016 (left panels) and 2017 (right panels). The y-axis represents the number of hours per day during which blue whale song was detected, and the x-axis represents the date. Grayed out sections represent gaps in recording due to hydrophone refurbishment. Synchronous peaks in occurrence between hydrophones and the overlapping detection ranges of the hydrophones (MARU1–5) indicate calls are detected simultaneously at multiple locations.
Plot level results of the monitoring programme on strict forest reserves in Flanders (Belgium) – dendrometry and regeneration
<p>This dataset contains plot level data of the monitoring programme on strict forest reserves in Flanders (Belgium), derived from dendrometric measurements on standing trees (living/dead), lying deadwood and regeneration.</p> <p>The methodology used to collect and process the data is described in <a href="https://purews.inbo.be/ws/portalfiles/portal/41050863/Vandekerkhove_etal_2021_MonitoringProgrammeOnStrictForestReservesFlanders.pdf">Vandekerkhove et al., 2021</a>.</p> <p>The dataset contains data of 947 circular plots from 15 different forest reserves, and spans one to three consecutive forest inventories with a 10 year interval. </p> <p>The file “<strong>plotlevel_data.zip</strong>” contains 10 separate csv-files:</p> <ul> <li> <p><strong>dendro_by_(diam_)plot(_species).csv</strong> : basic stand structure attributes per plot and period (volume, basal area, number of trees per hectare, …) for living and dead standing trees (threshold DBH 5 cm) : overall values and values per diameter class and/or species </p> </li> <li> <p><strong>carbon_by_plot.csv</strong>: biomass and carbon stock related to living trees, per plot and period</p> </li> <li> <p><strong>logs_by_decay_plot(_species).csv</strong> : volume per hectare of lying deadwood per plot and period: overall values and values per decay stage and/or species </p> </li> <li> <p>r<strong>egeneration_by_plot(_height)(_species).csv</strong> : numbers per ha of young trees (seedlings up to trees with DBH 5cm) : values per plot and period: overall values and values per heightclass and/or species </p> </li> </ul> <p>Descriptions of all variables used in these csv-files are stored in the file “<strong>_metadata_plotlevel_results_dendro.xlsx</strong>”.</p> <p>The file “<strong>plotinfo.csv</strong>” contains exact coordinates of the plot centre, name of the forest reserve, survey number, year of survey, … See file "<strong>_metadata_plotinfo.xlsx</strong>" for detailed descriptions of the variables used.</p> <p> </p> <p><strong>For any inquiries or further information, please contact Kris.vandekerkhove@inbo.be or Anja.leyman@inbo.be</strong>. The provided csv-files will be updated as required to address any issues or include data from additional surveys. . Please check for updated versions periodically.</p> <p>For terms of use: see below.</p>
Abbildung 5 in Praktische Hinweise und Empfehlungen zur Anwendung von Malaisefallen für Insekten in der Biodiversitätserfassung und im Monitoring
Abbildung 5. Malaisefalle (Townes-Modell) im Bautyp des Entomologischen Vereins Krefeld mit Bemassung (© M. Sorg/EVK)
Dataset 5 - Reinforced concrete and soil monitoring system in operational environment
<p>The data demonstrate reinforced concrete and soil prototype sensing devices used in an operational environment. The data will be kept confidential for Intellectual Property and exploitation purposes until further notice. </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.