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Figure 1 from: Assou D, Segniagbeto GH, Radji R, Akiti J, Pando F (2018) Monitoring data of marine turtles on the Togolese coast during 2012–2013. ZooKeys 779: 109-118. https://doi.org/10.3897/zookeys.779.26967
Figure 1 Region of the record Datasets of sea turtles (coastal areas of Ghana, Togo and Benin).
Figure 4 from: Assou D, Segniagbeto GH, Radji R, Akiti J, Pando F (2018) Monitoring data of marine turtles on the Togolese coast during 2012–2013. ZooKeys 779: 109-118. https://doi.org/10.3897/zookeys.779.26967
Figure 4 Distribution of marine turtle occurrences by species and location on the Togolese coast.
uGIM: week monitorization data of a microgrid with five agents (10/04/19-16/04/19)
<p>uGIM is a microgrid intelligent management software that can represent individual microgrid’s players using a multi-agent approach. This dataset has data regarding a week (from 10-04-2019 to 16-04-2019) of a microgrid with five players (all offices). All agents have consumption and generation data. One of the agents also has sensor data, such as temperature, movement and humidity.</p> <p>In uGIM, agents are deployed in the player’s facilities using single-board computers. All the data in this dataset is read and stored in five single-board computers. Each agent integrates several resources. In this microgrid deployment, all resources use TCP/IP communication. However, uGIM supports more protocols, such as Modbus/RTU and Modbus/TCP.</p> <p>uGIM related publications:<br> - Gomes, L., Vale, Z., & Corchado, J. M. (2020). Microgrid management system based on a multi-agent approach: An office building pilot. Measurement: Journal of the International Measurement Confederation, 154. <a href="http://doi.org/10.1016/j.measurement.2019.107427">https://doi.org/10.1016/j.measurement.2019.107427</a><br> - Gomes, L., Vale, Z. A., & Corchado, J. M. (2020). Multi-Agent Microgrid Management System for Single-Board Computers: A Case Study on Peer-to-Peer Energy Trading. IEEE Access, 8, 64169–64183. <a href="http://doi.org/10.1109/ACCESS.2020.2985254">https://doi.org/10.1109/ACCESS.2020.2985254</a><br> - Gomes, L. (2020). μGIM - Microgrid intelligen management system based on a multi-agent approach and the active participation of end-users [Universidad de Salamanca]. <a href="http://doi.org/10.14201/gredos.144238">https://doi.org/10.14201/gredos.144238</a><br> - Gomes, L., Spínola, J., Vale, Z., & Corchado, J. M. (2019). Agent-based architecture for demand side management using real-time resources’ priorities and a deterministic optimization algorithm. Journal of Cleaner Production, 241, 118154. <a href="http://doi.org/10.1016/j.jclepro.2019.118154">https://doi.org/10.1016/j.jclepro.2019.118154</a></p> <p> </p> <p><em>(if you used this dataset in your publications, please send us your information so we can add your publication to the list above)</em></p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Figure 1 from: Bowser ML, Burr SJ, Davis I, Dubois GD, Graham EE, Moan JE, Swenson SW (2019) A test of metabarcoding for Early Detection and Rapid Response monitoring for non-native forest pest beetles (Coleoptera). Research Ideas and Outcomes 5: e48536. https://doi.org/10.3897/rio.5.e48536
Figure 1 Map of sampling locations generated using SimpleMappr (Shorthouse 2010).
Figure 1. Insect species account for 70 in The Importance of Insect Monitoring to Conservation Actions in Hawaii
Figure 1. Insect species account for 70% of Hawaii's total number of endemic animal species.
Figure 7 in Monitoring the survival rate of released chukars: a case study in Elmalı Cedar Forest, Antalya, Turkey
Figure 7. Total number of dead birds and predation by predatory mammals and birds per week.
Gunshot sound files and spectrogram images from passive acoustic monitoring data in Vietnam.
<h3><strong>Introduction</strong></h3> <p>This dataset was created to support benchmarking of automated gunshot detection models using 'torch for R'. The data includes recordings collected from Chu Mom Ray National Park in Vietnam combined with an open dataset of gunshots from Belize (Katsis et al. 2022). The dataset is organized into several folders containing .jpg images and their corresponding .wav audio clips. </p> <h3>Data Summary</h3> <p>The dataset is divided into three main categories based on the region and use case:</p> <ol> <li>imagesvietnamunbalanced: Contains spectrogram images and audio data collected from Chu Mom Ray National Park in Vietnam. These data are used for training and evaluating automated gunshot detection models.</li> <li>imagesvietnam_belize: Includes spectrogram images and audio data from both Vietnam and Belize. The data in this folder is used for benchmarking model performance across different geographical regions.</li> <li>testdatacombined: Separate test data to evaluate performance with a large number of noise clips, representing real-world automated detection scenarios.</li> </ol> <p>Each .jpg image in these directories is associated with a .wav file representing a corresponding audio clip. The .wav files were recorded using passive acoustic monitoring and clips were isolated using manual annotations in Raven Pro Software.</p> <p>If used please cite: </p> <p>Vu, T. T., Phan, D. V., Le, T. S., & Clink, D. J. (2024). Investigating hunting in a protected area in Southeast Asia using passive acoustic monitoring with mobile smartphones and deep learning. <em>Ecological Indicators. </em></p> <p>Vu, T. T., Phan, D. V., Le, T. S., & Clink, D. J. (2024). Gunshot sound files and images from passive acoustic monitoring data in Vietnam. [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13893977" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13893977</a></p> <p>Katsis, Lydia; Hill, Andrew; Piña-Covarrubias, Evelyn; Prince, Peter; Rogers, Alex; Doncaster, C. Patrick; Snaddon, Jake (2022), “Tropical forest gunshot classification training audio dataset”, <em>Mendeley Data</em>, V3, doi: 10.17632/x48cwz364j.3</p>
A first attempt of laminate geometry for integration of different structural health monitoring techniques
Open the record for dataset details and reuse information.
Data from: Long-term monitoring dataset of fish assemblages impinged at nuclear power plants in northern Taiwan
The long-term species diversity patterns in marine fish communities are garnering increasing attention from ecologists and conservation biologists. However, current databases on quantitative abundance information lack consistent long-term time series, which are particularly important in exploring the possible underlying mechanism of community changes and evaluating the effectiveness of biodiversity conservation measures. Here we describe an impinged fish assemblage dataset containing 1, 283, 707 individuals from 439 taxa. Once a month over 19 years (1987–1990 and 2000–2014), we systematically collected the fish killed by impingement upon cooling water intake screens at two nuclear power plants on the northern coast of Taiwan. Because impingement surveys have low sampling errors and can be carried out over many years, they serve as an ideal sampling tool for monitoring how fish diversity and community structure vary over an extended period of time.
Fig. 2 in Collaborative monitoring of the ornamental trade of seahorses and pipefishes (Teleostei: Syngnathidae) in Brazil: Bahia State as a case study
Fig. 2. Hippocampus reidi. Photo (inverted): Ary Amarante.
Abundance of benthic sessile fauna associated with ARMS from the research" Evaluation of the use of Autonomous Reef Monitoring Structures (ARMS) for capturing the biological diversity of two coral reefs in the Yucatán Península, México " using the point intersection method (400 points per plate on each side) with the CPCe v4.1 soft-ware (Coral Point Count with Excel extension v4.1)
<p><strong>*Corresponding autor: </strong>edlinguerra@gmail.com</p> <p>This database contains information on the abundance of benthic sessile fauna associated with ARMS from the research “Evaluation of the use of Autonomous Reef Monitoring Structures (ARMS) to estimate cryptic diversity in two coral reefs of the Yucatan Península, México” using the point intersection method (400 points per plate on each side) with the CPCe v4.1 soft-ware (Coral Point Count with Excel extension v4.1) to analyze </p> <p>Krebs, C.J. Estimating Density: Quadrats Counts. In <em>Ecological Methodology</em>; Addison-Wesley Educational Publishers, Inc., 1999; p. 69 ISBN 0321021738</p> <p>Kohler, K.; Gill, S. Coral Point Count with Excel extensions (CPCe): A Visual Basic program for the determination of coral and substrate coverage using random point count methodology. <em>Comput. Geosci.</em> <strong>2006</strong>, <em>32</em>, 1259–1269</p> <div> <p><span lang="EN-US">was produced in collaboration with the Biodiversidad Marina de Yucatán project. </span><a href="https://www.bdmy.org.mx/carteles-publicaciones/" target="_blank" rel="noopener">https://www.bdmy.org.mx/,</a> Universidad Nacional Autonoma de México, Harte Research Institute for Gulf of Mexico Studies and Escuela Nacional de Estudios Superiores</p> </div>
Figure 6 from: Petrović-Obradović O (2013) Monitoring of aphid flight activities in seed potato crops in Serbia. ZooKeys 319: 333-346. https://doi.org/10.3897/zookeys.319.4315
Figure 6 - Vector pressure index for PLRV and most important vector per locality.
Figure 5 from: Petrović-Obradović O (2013) Monitoring of aphid flight activities in seed potato crops in Serbia. ZooKeys 319: 333-346. https://doi.org/10.3897/zookeys.319.4315
Figure 5 - Vector pressure index for PVY and most important vector per locality.
Figure 3 from: Petrović-Obradović O (2013) Monitoring of aphid flight activities in seed potato crops in Serbia. ZooKeys 319: 333-346. https://doi.org/10.3897/zookeys.319.4315
Figure 3 - Total Shannon–Weaver index diversity per locality.
Figure 2 from: Weijola V, Donnellan SC, Lindqvist C (2016) A new blue-tailed Monitor lizard (Reptilia, Squamata, Varanus) of the Varanus indicus group from Mussau Island, Papua New Guinea. ZooKeys 568: 129-154. https://doi.org/10.3897/zookeys.568.6872
Figure 2 - Tongue color of the freshly collected Varanus semotus holotype ZMUT Sa176.
Figure 3 from: Dvorak TM, Catalano AE, Guilliams CM (2016) Monitoring within non-native ungulate exclosures documents the inherent size of Crocanthemum greenei (Cistaceae). PhytoKeys 70: 11-16. https://doi.org/10.3897/phytokeys.70.9363
Figure 3 - A severely browsed individual from a population not protected by exclosure fencing.
Advanced monitoring of tailings dam performance - data and notebooks
<p>Seismic data used to recreate figures in Ouellet et al. 2022 'Advanced monitoring of tailings dam performance using seismic noise and stress models'.</p> <p>Original analysis completed on undecimated (500Hz) seismic data. Uploaded seismic data (miniseed) files have been decimated to 50Hz due to file size constraints.</p> <p>See <a href="http://github.com/smouellet/dVsVs_tailingsmonitoring ">https://github.com/smouellet/dVsVs_tailingsmonitoring </a> for the associated code and notebooks.</p>
Using genomics and morphometrics to monitor data-poor and commercially exploited octopod populations
<p>Raw demultiplexed data as fastq.gz files</p> <p>Processed files for each species dataset (Neutral, Selection, Autosomal)</p> <p>Mastersheet containing sample information, GenBank accession numbers, morphological raw data, haplotype number, and whether samples were used in the SNP analyses</p> <p>Codes for morphological and SNP analyses</p> <p>Metadata</p>
Datasets used in Detecting Plumes in Mobile Air Quality Monitoring Time Series with Density-based Spatial Clustering of Applications with Noise v01
<p>This repository contains the following data sets related to Detecting Plumes in Mobile Air Quality Monitoring Time Series with DBSCAN published in . Please cite the following: .</p> <p>Validated_Data.csv: A .csv file containing the validation set used in the study. Column headings are the following:</p> <p>"Lat1": GPS latitude of car location in degrees.<br> "Long1": GPS longitude of car location in degrees.<br> "LST": Measurement time stamp. Time zone US/Central.<br> "BC": Black carbon measurements in ng/m^3<br> "CO2": Carbon dioxide measurements in ppm.<br> "UFP": Ultrafine particle count in particles/cc.<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2, in ppb.<br> "Anomaly": What has been manually flagged as "Anomaly" (2) or "Normal" (1).<br> "Uniq_Fac": Factor from 1-30 mapping to different days of the campaign. For example, all measurements with Uniq_Fac = 1 belong to the same day.</p> <p>Labeled_DBSCAN_Anomalies.csv: A .csv file containing points labeled as anomalies by the DBSCAN algorithm described in the manuscript. Columns are the following.</p> <p>"BC": Black carbon measurement (ng/m^3)<br> "CO2": Carbon dioxide measurement (ppm)<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2 (ppb)<br> "UFP": Ultrafine particle count (p/cc)<br> "Anomaly": Whether the DBSCAN algorithm has labeled this point as "Anomaly" (2) or "Normal" (1)<br> "Uniq_Fac": Factor spanning from 1-277 grouping measurements taken on separate days by car. E.g. all measurements with Uniq_Fac=1 were grouped and analyzed together.<br> "LST": Timestamp (US/Central)<br> "Road_Class": TigerLINE census road class designation for the given point. Possible road classes are S1100 - Primary Road, S1200 - Secondary Road, S1400 - Local Road, S1630 - Ramps, S1640 - Service Drives, S1730 - Private Roads<br> "X": Universal Transverse Mercator Easting for Zone 15N (m).<br> "Y": Universal Transverse Mercator Northing for Zone 15N (m).</p> <p>*_To_Be_Validated.csv: A series of files where * denotes the following.</p> <p>"DB": DBSCAN Algorithm<br> "QOR": QOR Algorithm<br> "QAND": QAND Algorithm<br> "Drew": Drewnick Algorithm</p> <p>Each file contains the following columns:</p> <p>"Lat1": GPS latitude of car location in degrees.<br> "Long1": GPS longitude of car location in degrees.<br> "LST": Measurement time stamp. Time zone US/Central.<br> "BC": Black carbon measurements in ng/m^3<br> "CO2": Carbon dioxide measurements in ppm.<br> "UFP": Ultrafine particle count in particles/cc.<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2, in ppb.<br> "Anomaly": What has been flagged as "Anomaly" (2) or "Normal" (1).<br> "Uniq_Fac": Factor from 1-30 mapping to different days of the campaign. For example, all measurements with Uniq_Fac = 1 belong to the same day.</p>
Data for the paper "A Multi-station Meteor Monitoring (M³) System. I. Design and Testing"
<p>This repository contains following data:</p> <p>1.Meteor observation data:</p> <p>JSON files of observation reports, including time, location of station, meteor ECI coordinates etc.</p> <ul> <li>folder: observations/HH: JSON files of station A</li> <li>folder: observations/YQ: JSON files of station A</li> </ul> <p>2.Meteoroid orbit data:</p> <p>Orbit data generated by WMPL (https://github.com/wmpg/WesternMeteorPyLib) for 473 meteoroids. Each folder contains reports and charts for success runs, and error massages if unsuccessful.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.