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Fig. 2 in Survey of Florida olive groves during olive fruit development: monitoring for stink bugs and olive fruit flies
Fig. 2. Comparison of type of stink bug on total monthly collection (mean ± SE) from 2017 and 2018 of (A) Hemiptera, and (B) Euschistus quadrator Rolston. Lures consisted of either a consperse stink bug lure or a combination of green stink bug and brown marmorated stink bug lures. Statistical comparisons were made with a paired t-test (P ≤ 0.05); means with different letters are significantly different.
Fig. 1 in Survey of Florida olive groves during olive fruit development: monitoring for stink bugs and olive fruit flies
Fig. 1. Diagram of (A) trap locations in Florida olive groves, and (B) spatial identifiers used to characterize the locations for analysis. The large rectangle represents a 4-ha area surveyed; each white box represents a 1-ha subplot. Each circle represents a sampling location where 1 baited olive fruit fly trap and 2 dual funnel stink bug traps, 1 baited for the brown marmorated stink bug and 1 baited for the consperse stink bug were placed during each sampling visit. Spatial identifiers for sampling sites included: COR = corner site, CEN = center site, ER = edge of the grove site and bordered by olive trees on 3 sides of the tree, END = site located at the end of a row, but not a corner. Image from Google Maps.
Fig. 1 in The ecology, distribution, status, threats, and conservation of the Common Water Monitor (Varanus salvator) in the Dhaleswari River of Assam, India
Fig. 1. (A) Map of India, highlighting Assam. (B) Map of Assam, highlighting Hailakandi district. (C) Map of Hailakandi district showing the two rivers, Dhaleswari River and Katakhal River, with distribution of the Common Water Monitor (Varanus salvator). Dots represent sighting locations during the present survey; current distribution in the Dhaleswari River is shown in green. Further downstream, despite no present records, occurrence in the past (1970s–1980s) was reported by several interviewees (shown in yellow). No reports on present occurrence in the Katakhal River (shown in red) could be found. Map by A.S. Choudhury.
Fig. 2 in The ecology, distribution, status, threats, and conservation of the Common Water Monitor (Varanus salvator) in the Dhaleswari River of Assam, India
Fig. 2. Photographs relevant to the habitat and threats of the Common Water Monitor (Varanus salvator) in the Dhaleswari River, Assam, India. (A) Research team interacting with the locals at Rongpur 5, Hailakandi. (B) Habitat of the Common Water Monitor in the Dhaleswari River, showing bushes and other features on the banks. (C) The sandy bank of the Katakhal River, prone to erosion and landslides, is the habitat not preferred by the Common Water Monitor. (D) The sluice gate at the mouth of Dhaleswari River at Shahabad, which prevents water flow into it and diverts the water to the Katakhal River. (E) Encroachment and conversion of the Dhaleswari River into fisheries by the locals building dikes at Rongpur 2. Photos by A.S. Choudhury.
Fig. 3. A in The ecology, distribution, status, threats, and conservation of the Common Water Monitor (Varanus salvator) in the Dhaleswari River of Assam, India
Fig. 3. A Common Water Monitor (Varanus salvator) killed for venturing into a human habitation at Rongpur 6, Hailakandi. It was subsequently buried. Photo by R.A. Barbhuiya.
Fig. 3 in Short-term spider community monitoring after cattle removal in grazed grassland
Fig. 3. Richness based rarefaction curves for spiders sampled in ungrazed areas of APA Ibirapuitã, Rio Grande do Sul state, Brazil, trough springs of 2011, 2012 and 2013. Adjacent lines indicates 95% confidence intervals.
Fig. 2 in Short-term spider community monitoring after cattle removal in grazed grassland
Fig. 2. Format used for exposal of pitfall traps in APA Ibirapuitã, state of Rio Grande do Sul, Brazil during the campaign of 2011, 2012 and 2013. Red diamonds indicates the places of the traps. Traps were placed around 20 m from each other and least 10 m from the fence or border of each plot.
Fig. 1 in Short-term spider community monitoring after cattle removal in grazed grassland
Fig. 1. Extension of the Pampa Biome at Neotropical region. Red triangle indicates APA Ibirapuitã's localization, state of Rio Grande do Sul, Brazil. Map from ANDRADE et al., 2015.
Fig. 5 in Veterinary monitoring of gastrointestinal parasites in European bison, Bison bonasus designated for translocation: Comparison of two coprological methods
Fig. 5. The relationship between the prevalence of Eimeria spp. oocysts in European bison feces measured by the Willis and modified McMaster techniques (each point represents an individual parasite species).
Fig. 1 in Veterinary monitoring of gastrointestinal parasites in European bison, Bison bonasus designated for translocation: Comparison of two coprological methods
Fig. 1. Probability of detection of Eimeria spp. oocysts with the modified McMaster technique based on the number of oocysts detected using the Willis technique.
Fig. 4 in Veterinary monitoring of gastrointestinal parasites in European bison, Bison bonasus designated for translocation: Comparison of two coprological methods
Fig. 4. The relationship between the prevalence of various taxa eggs/oocysts in European bison feces measured by the Willis and modified McMaster techniques (each point represents an individual taxon/genus, blue points stand for oocysts and red point for eggs). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in Veterinary monitoring of gastrointestinal parasites in European bison, Bison bonasus designated for translocation: Comparison of two coprological methods
Fig. 3. Probability of detection of Trichostrongylidae eggs with the modified McMaster technique based on the number of eggs detected using the Willis technique.
Fig. 2 in Veterinary monitoring of gastrointestinal parasites in European bison, Bison bonasus designated for translocation: Comparison of two coprological methods
Fig. 2. Probability of detection of Trichuris sp. eggs with the modified McMaster technique based on the number of eggs detected using the Willis technique.
Geodetic Monitoring of the Recent Activity and the Dome Forming Eruption at Nevado del Ruiz (Colombia), 2010-2023
<p><a name="_Hlk160011067"></a>Nevado del Ruiz volcano in Colombia is known for the catastrophic eruption of 1985 that destroyed the villages of Armero and Chinchiná. This was not its first destructive event; similar eruptions occurred in 1595 and 1845. Unfortunately, the sparse geodetic data available in 1985 did not provide a clear warning of an impending eruption. Since 1985, geodetic monitoring has evolved, incorporating tilt, GNSS and InSAR. Combined with seismic, geochemical, and geological monitoring, these techniques have enhanced the understanding of the volcano, improved hazards assessment and mitigated the risk from the eruptions of 1989, 2012, and 2015-2019, as well as the unrest over the last 11 years. Modeling of deformation data over the past 13 years reveals complex interactions between local and regional magma reservoirs. Before February 2012, the volcano deflated, initially thought to be due to gravitational collapse of the edifice, but now attributed to the depressurization of a shallow magma reservoir. This reservoir has been feeding ash emissions, gas release (2012-2023), and the dome forming eruption (2015-2019). The inflation of the volcanic edifice from 2012 to 2023 is linked to a deep pressurization source beneath the Nevado de Santa Isabel volcano, about 9 km southwest of Nevado del Ruiz.</p>
Forest Monitoring Dataset
<p>This file contains the dataset used in our work <strong><a href="https://arxiv.org/abs/2407.13102">Tree semantic segmentation from aerial image time series</a>. </strong></p> <p><strong>Ramesh, V., Ouaknine, A., & Rolnick, D. (2024). Tree semantic segmentation from aerial image time series. <em>arXiv [Cs.CV]</em>. Retrieved from http://arxiv.org/abs/2407.13102.</strong></p> <p>The dataset contains the tiled images and labels in png format, along with reference CSV's for the train-validation-test splits.</p> <p><strong>Abstract</strong></p> <p>Earth's forests play an important role in the fight against climate change, and are in turn negatively affected by it. Effective monitoring of different tree species is essential to understanding and improving the health and biodiversity of forests. In this work, we address the challenge of tree species identification by performing semantic segmentation of trees using an aerial image dataset spanning over a year. We compare models trained on single images versus those trained on time series to assess the impact of tree phenology on segmentation performances. We also introduce a simple convolutional block for extracting spatio-temporal features from image time series, enabling the use of popular pretrained backbones and methods. We leverage the hierarchical structure of tree species taxonomy by incorporating a custom loss function that refines predictions at three levels: species, genus, and higher-level taxa. Our findings demonstrate the superiority of our methodology in exploiting the time series modality and confirm that enriching labels using taxonomic information improves the semantic segmentation performance.<br><br><br></p>
Surface Marine Carbonate System Data (2019-2024) from Volunteer Observing Ship monitoring across the Western Mediterranean Sea
<p><strong><span><span>1.<span> </span></span></span></strong><strong><span>Introduction</span></strong></p> <p><span>We present here a high-resolution dataset which spans five years (February 2019 - February 2024) and is based on weekly physicochemical observations of the surface waters along the western boundary of the Mediterranean Sea. Data were automatically collected by a Surface Ocean Observation Platform (SOOP) operating in underway mode aboard the Volunteer Observing Ship (VOS) MV JONA SOPHIE (formerly RENATE P until November 2021), a container ship managed by Nisa Maritima on the route between the Canary Islands and Barcelona. A total of 92 routes were completed in the Mediterranean Sea during the observation period.</span></p> <p><span>The SOOP CanOA-VOS line, designed and maintained by the QUIMA research group at IOCAG-ULPGC, is part of Spain’s contribution to the Integrated Carbon Observation System (ICOS-ERIC) since 2021 and is recognized as an ICOS Class 1 Ocean Station, ensuring that the measurement equipment and data collection techniques meet ICOS-ERIC's high-quality standards and methodological recommendations. The data collected is also available at the ICOS Data Portal (<a href="https://www.icos-cp.eu/data-products/ocean-release">https://www.icos-cp.eu/data-products/ocean-release</a>).</span></p> <p><strong><span><span>2.<span> </span></span></span></strong><strong><span>Data collection</span></strong></p> <p><span>The dataset includes continuous monitoring of CO<sub>2</sub> levels in both surface ocean and low atmosphere, following protocols to ensure data comparability and quality given by Pierrot et al., (2009). A detailed description is provided by Curbelo-Hernández et al., 2021a, 2021b). An automated CO2 molar fraction (xCO2) measurement system, developed by Craig Nail and commercialized by General Oceanics™, was installed on the ship. This system integrates air and seawater equilibrators with a non-dispersive infrared analyzer by LICOR® for xCO2 detection. The analyzer is regularly calibrated using standard gases provided by the NOAA, traceable to the World Meteorological Organization (WMO). They were in the order of 0 ppm, 250 ppm, 400 ppm and 550 ppm until January 2021, when the gas bottles for standard 2 to 4 were changed for a new set with concentrations in the order of 300 ppm, 500 ppm and 800 ppm. </span></p> <p><span>In addition to xCO2, sea surface temperature (SST) and sea surface salinity (SSS) were monitored using high-precision instruments. The SST was monitored by using a SBE38 thermometer placed at the main seawater intake in the engine room, with a reported error of ±0.01ºC. A SBE45 thermosalinograph and a Hart Scientific HT1523 Handheld Thermometer, with reported errors of ±0.01ºC, were used to monitor the temperature at the entrance of the wet box and inside the equilibrator, respectively. The SBE45 thermosalinograph measured the sea surface salinity (SSS) with an estimated error of ±0.005.</span></p> <p><span>Discrete seawater samples were also collected during three round trips in February 2020, March 2021, and October 2023, for further analysis of total alkalinity and dissolved inorganic carbon. </span><span>A total of 102 discrete samples has been collected in the Mediterranean Sea. Total alkalinity (AT) and total inorganic carbon (CT) were determined using a VINDTA 3C according to Mintrop et al., 2000. AT was analyzed via potentiometric titration with HCl, following the carbonic acid endpoint method (Millero et al., 1993; Dickson and Goyet, 1994), while CT was determined through coulometric titration (Johnson et al., 1993). The VINDTA 3C was calibrated using Certified Reference Material (CRMs) by A. Dickson, ensuring an accuracy of ±1.5 </span><span>μ</span><span>mol kg-1 for AT and ±1.0 </span><span>μ</span><span>mol kg-1 for CT.</span></p> <p><span>The dataset contains some gaps, including a year-long gap from September 2021 to 2022 due to vessel maintenance and shorter gaps due to technical issues, which were addressed during routine maintenance. Some technical issues in 2020 were delayed due to COVID-19 constraints.</span></p> <p><strong><span>3. Dataset content</span></strong></p> <p><span>The dataset includes the following variables: </span></p> <p><span><span>·<span> </span></span></span><span>“Date” (dd/mm/yyyy)</span></p> <p><span><span>·<span> </span></span></span><span>“Time” (hh:mm:ss)</span></p> <p><span><span>·<span> </span></span></span><span>“Latitude”</span></p> <p><span><span>·<span> </span></span></span><span>“Longitude”</span></p> <p><span><span>·<span> </span></span></span><span>“equTemp”: seawater temperature measured inside the equilibrator using a Hart Scientific HT1523 Handheld Thermometer.</span></p> <p><span><span>·<span> </span></span></span><span>“stdVal”: value of the standard gases used for automatically calibrations.</span></p> <p><span><span>·<span> </span></span></span><span>“xCO2”: measured CO2 molar fraction without performing any correlation (raw data).</span></p> <p><span><span>·<span> </span></span></span><span>“atmPress”: Atmospheric pressure (units: atm).</span></p> <p><span><span>·<span> </span></span></span><span>“equPress”: Diffeence in pressure between the atmosphere and the equilibrator (units: atm).</span></p> <p><span><span>·<span> </span></span></span><span>“SST_SBE38”: Sea surface temperature measured with a SBE38 thermometer at the main seawater intake of the vessel.</span></p> <p><span><span>·<span> </span></span></span><span>“Temp_SBE45”: Seawater temperature measured with a SBE45 thermosalinograph just before the water supply to the equilibrator.</span></p> <p><span><span>·<span> </span></span></span><span>“SSS_SBE45”: Sea surface salinity measured with a SBE45 thermosalinograph just before the water supply to the equilibrator.</span></p> <p><span><span>·<span> </span></span></span><span>“xCO2corr_sw”: CO2 molar fraction measured in the sea surface after correction by using standard gases.</span></p> <p><span><span>·<span> </span></span></span><span>“xCO2corr_atm”: CO2 molar fraction measured in low atmosphere after correction by using standard gases.</span></p> <p><span><span>·<span> </span></span></span><span>“pCO2sw”: Partial pressure of CO2 in the sea surface (units: µatm).</span></p> <p><span><span>·<span> </span></span></span><span>“pCO2atm”: Partial pressure of CO2 in low atmosphere (units: µatm).</span></p> <p><span><span>·<span> </span></span></span><span>“fCO2sw”: fugacity of CO2 in the sea surface (units: µatm).</span></p> <p><span><span>·<span> </span></span></span><span>“fCO2atm”: fugacity of CO2 in low atmosphere (units: µatm).</span></p> <p><span><span>·<span> </span></span></span><span>“AT”: Total Alkalinity (µmol kg-1)</span></p> <p><span><span>·<span> </span></span></span><span>“CT”: Total Inorganic Carbon (µmol kg-1)</span></p> <p><span><span>·<span> </span></span></span><span>“pH”: pH in surface seawater at in situ temperature computed in CO2sys using as input variables AT and fCO2sw.</span></p> <p><span><span>·<span> </span></span></span><span>“pH25”: pH in surface seawater at constant temperature of 25ºC computed in CO<sub>2sys</sub> using as input variables AT and fCO2sw.</span></p> <p><span> </span></p> <p><strong><span>Acknowledgement</span></strong></p> <p><span>This research was supported by the Canary Islands Government and the Loro Parque Foundation through the CanBIO project, CanOA subproject (2019–2024), and the CARBOCAN agreement (Consejería de Transición Ecológica y Energía, Gobierno de Canarias). We would like to thank the JONA SOPHIE ship owner, the NISA-Marítima company and the captains and crew members for the support during this collaboration. Special thanks to the technician Adrian Castro-Alamo for biweekly equipment maintenance and discrete sampling of total alkalinity aboard the ship. The SOOP CanOA-VOS line is part of the Spanish contribution to the Integrated Carbon Observation System (ICOS-ERIC; https://www.icos-cp.eu/) since 2021 and has been recognized as an ICOS Class 1 Ocean Station. <span>The participation of D. C-H was funded by the PhD grant PIFULPGC-2020-2 ARTHUM-2</span></span></p>
Datasets for time-lapse camera monitoring of insects and their floral environments
<p>Contains the dataset for training and validation of models to estimate flower cover and identify taxa of arthropods in time-lapse camera recordings described in the paper:</p> <p>Kim Bjerge, Henrik Karstoft, Hjalte M. R. Mann, Toke T. Høye, A deep learning pipeline for time-lapse camera monitoring of insects and their floral environments, 2024, bioRxiv, <a href="https://doi.org/10.1101/2024.04.12.589205" rel="noopener">https://doi.org/10.1101/2024.04.12.589205</a></p> <p>The zip files contain the needed files and directory structure to train the models in Python code published at: <a href="https://github.com/kimbjerge/insectsFlowers">https://github.com/kimbjerge/insectsFlowers</a></p> <p>Content of zip files:<br>===============</p> <p>insects.zip: Contains images and labels in YOLO format: <a href="https://github.com/ultralytics/yolov5/issues/2293">https://github.com/ultralytics/yolov5/issues/2293</a></p> <p>trainI21m contains the images and labels to train the insect detector with YOLOv5. Contains only the motion-informed enhanced images (MIE).<br>testI21m contains the images and labels to test the insect detector trained with YOLOv5. Contains only the motion-informed enhanced images (MIE).</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Flowers.zip: contains the images of plants and flowers with black and white masks to train the DeepLabv3 flower semantic segmentation model.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>NI2-19cls.zip: contains images for training and validation of the arthropod classifiers </p> <p>Image crops of arthropods are organized in 19 subdirectories one for each class.</p> <p>A1-Coccinellidae<br>B2-Coleoptera<br>C3-Background<br>D4-Bombus<br>E5-Syrphidae<br>F6-Lepidoptera<br>G7-Aranaeae<br>H8-Formidicidae<br>I9-Diptera<br>J10-Hemiptera<br>K11-Isopoda<br>L12-Uspecificerede<br>N13-Hymenoptera<br>O14-Orthoptera<br>P15-Rhagonycha_fulva<br>Q16-Satyrinae<br>R17-Aglais_urticea<br>S18-Odonata<br>T19-Apis_mellifera</p>
Data from multi-sensor devices and reference station to monitoring urban air quality
<p>Data from electrochemical and optical sensors.</p> <h3>Files names</h3> <ul> <li>ECT01, ECT02, ECT06, ECT07 = device name</li> <li>ISSEP = reference station <ul> <li>"c" = calibration data</li> <li>"v" = validation data</li> </ul> </li> </ul> <h3>Variable description</h3> <table> <tbody> <tr> <td><strong>Electrochemical sensor</strong></td> <td><strong>Optical sensor</strong></td> <td><strong>Probe</strong></td> <td><strong>Reference</strong></td> </tr> <tr> <td> <p>AE = auxiliary electrode (mV)</p> <p>WE = working electrode (mV)</p> <p>N = temperature correction </p> <ul> <li>ch0 = CO sensor</li> <li>ch1 = OX sensor</li> <li>ch2 = NO2 sensor</li> <li>ch3 = NO sensor</li> </ul> </td> <td> <p>PM1, PM2.5 and PM10 in µg/m³</p> </td> <td> <p>Prs_mbar = pressure (mbar)</p> <p>Temp = temperature (°C)</p> <p>RH = relative humidity (%)</p> </td> <td> <p>DV30 = wind direction @ 30m (°)</p> <p>HR = relative humidity (%)</p> <p>NO, NO2, O3, PM10 and PM2.5 (µg/m³)</p> <p>Precipita = precipitation (mm)</p> <p>TC3 = temperature @ 3m (°C)</p> <p>VV30 = wind speed @ 30m (m/s)</p> </td> </tr> </tbody> </table> <p> </p>
Dataset: Structural Health Monitoring of a Flexible Wing
<p>This Zenodo entry contains the experimental data underlying the journal article Noise-robust Modal Parameter Identification and Damage Assessment for Aero-structures, in preparation. The document XB-2_SHM_Dataset.pdf serves as the explanatory note for the data contained in SHM_XB2.mat</p>
DiY Sensor Dataset for Air Pollution Monitoring
<p>The research engaged students from a private university in Bilbao in designing and implementing a project that involved young students in collecting and analyzing air quality data using air meters they assembled. An interesting aspect of the project was the development of an image processing technique for analyzing dust captured on petroleum jelly, enhancing the quantification of particulate matter and providing insights into air quality at various school locations. Additionally, the study introduced a synthetic data generation algorithm designed to simulate air quality data for educational purposes, which allowed students to engage with data analysis and interpretation, thereby enriching their learning experience and understanding of air pollution dynamics.</p>
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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.