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Supplementary material 2 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
RTL Genomics Data Analysis Methodology
Figure 1 in Necromys lasiurus (Cricetidae: Sigmodontinae) from open areas of the Atlantic Forest of Rio de Janeiro: Population structure and implications for the monitoring of hantaviruses
Figure 1. Distribution of Necromys lasiurus sample groups by ecoregion. The points are color-coded by ecoregion and sample group. Insert map shows the Brazilian biomes.
Fig. 2 in The Use of Testate Amoebae in Monitoring Peatland Restoration Management: Case Studies from North West England and Ireland
Fig. 2. Top: The sampling site on Astley bog in 1998 when the samples described in Davis and Wilkinson (2004) were taken. Bottom: Danes Moss in 1999 when the samples described in Davis and Wilkinson (2004) were taken.
Fig. 6. A in The Use of Testate Amoebae in Monitoring Peatland Restoration Management: Case Studies from North West England and Ireland
Fig. 6. A: Surface testate samples from a raised 'hummock' site on Holcroft Moss. B: Surface testate samples from bare peat on a path at Holcroft Moss. Note that in the older literature (including all the more accessible identification guides) Archerella flavum is refered to as Amphitrema flavum.
Fig. 1 in The Use of Testate Amoebae in Monitoring Peatland Restoration Management: Case Studies from North West England and Ireland
Fig. 1. Left: map of UK and Ireland showing locations of sites mentioned in text. Right Top: NW England, showing 1. Chat Moss (Astley Moss as cut-out), 2. Holcroft Moss and 3. Danes Moss. Right Bottom: Location of Ardagullion Bog, Co. Longford.
Fig. 3 in The Use of Testate Amoebae in Monitoring Peatland Restoration Management: Case Studies from North West England and Ireland
Fig. 3. Top: A general view across the current (in 2012) surface of Holcroft Moss from close to our core site. Bottom: The edge of Holcroft Moss (in 2011) showing both the fence designed to keep the sheep used for conservation grazing on the bog and – between the fence and the taller vegetation – the plastic piling sunk into the peat to try and maintain a wetter bog surface.
Figure 5 in Testing the Attractiveness and Efficacy of Baits for the Monitoring and Control of the Thief Ant, Solenopsis papuana
Figure 5. Mean (±SE) number of S. papuana in field plots treated with Amdro® Ant Block® and Siesta™ baits, in comparison to untreated control plots. Bait stations were installed in the Amdro® Ant Block® and Siesta™ plots on the first monitoring date (3 July 2015) immediately after monitoring, and baits were replaced on each subsequent monitoring event except the final date (5 March 2016).
Figure 6 in Testing the Attractiveness and Efficacy of Baits for the Monitoring and Control of the Thief Ant, Solenopsis papuana
Figure 6. Mean (±SE) reduction in numbers of S. papuana 28 days after bait station placement in the field plots, categorized by distance of monitoring points from pesticide bait stations. There was no significant difference (based on ANOVA, α=0.05) in degree of reduction among distances for either ant bait.
Figure 2 in Testing the Attractiveness and Efficacy of Baits for the Monitoring and Control of the Thief Ant, Solenopsis papuana
Figure 2. Mean number (±SE) of S. papuana attracted to food baits at Lyon (top) and Pahole (bottom) over the course of three hours. Means sharing the same letters within each hour at each site are not significantly different (Welch's ANOVA and GamesHowell posthoc test on log-transformed counts, α=0.05; depicted means and SEs are back-transformed).
Hearing the Voice of Software Practitioners on Technical Debt Monitoring: Understanding Monitoring Practices and the Practices' Avoidance Reasons: Data set
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Monitoring Solar Irradiance and PV Module Performance in Mobile Applications
<p>Video footage from the car in the PV in motion experiment.</p>
Towards Enhancing Field-Based Vegetation Monitoring: A Deep Learning Approach for Species Identification and Coverage Estimation from Ground-level Imagery
<h1>🌿Species Identification and Coverage Estimation from Ground-level Imagery for Vegetation Monitoring 📷</h1> <p>This repository contains the data and code used in <a href="https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.70024"><strong>Müller, Puliti, and Breidenbach (2025)</strong></a> to train and apply deep learning models for <strong>species coverage estimation</strong> using ground-level imagery.</p> <p>It includes:<br>✅ A <strong>YOLOv8 object detection </strong>model for detecting frames in images and one <strong>species instance segmentation</strong> model for species identification and identifying and segmenting species.</p> <p>✅ Method to parse instance segmentation masks to <strong>species-specific coverage estimates</strong> in images.</p> <p>✅ The <strong>data </strong>used to train and evaluate the models</p> <p> </p> <h2>🚀 Workflow Overview</h2> <p>This demo provides a step-by-step approach for training and applying the models:</p> <p>1️⃣ <strong>Training</strong>:</p> <ul> <li>Train two models using labeled images: <ul> <li><strong>Frame Object Detection</strong> (dataset: <code>Frame_data</code>)</li> <li><strong>Species Instance Segmentation</strong> (dataset: <code>Species_segmentation_data</code>)</li> </ul> </li> </ul> <p>2️⃣ <strong>Confidence Optimization</strong>:</p> <ul> <li>Optimize the confidence threshold based on downstream <strong>cover estimation</strong> performance.</li> </ul> <p>3️⃣ <strong>Inference</strong>:</p> <ul> <li>Predict on test images (<code>Species_cover_data_test</code>).</li> </ul> <p>4️⃣ <strong>Evaluation</strong>:</p> <ul> <li>Compare predictions with <strong>field estimates</strong> (<code>Field_data_NFI</code>).</li> </ul> <p>📌 The code has been tested on <strong>Windows</strong> with <strong>Python 3.10</strong>.</p> <p> </p> <h2>🛠 How to Run the Demo</h2> <p>Follow these steps to set up and run <code>demo.ipynb</code>:</p> <div> <div> <div> </div> </div> <div> <blockquote> <p># Create a new environment<br>conda create -n VegCover python=3.10</p> <p># Activate the environment<br>conda activate VegCover</p> <p># Install dependencies<br>pip install -r requirements.txt</p> <p># Install Jupyter Lab<br>pip install jupyterlab</p> <p># Open the demo notebook<br>jupyter-lab</p> </blockquote> </div> </div> <h2> </h2> <h2>📖 How to Cite</h2> <p>If you use this work, please cite:</p> <p><strong>Müller, P., Puliti, S., & Breidenbach, J. (2025).</strong> Towards Enhancing Field-Based Vegetation Monitoring: A Deep Learning Approach for Species Coverage Estimation from Ground-Level Imagery. <em>Methods in Ecology and Evolution.</em></p> <h2> </h2> <h2>📜 License</h2> <p>This project is licensed under the <strong>GNU Affero General Public License v3.0 or later (AGPL-3.0-or-later)</strong>.</p> <p>🔹 <strong>Key points of this license:</strong></p> <ul> <li>You are free to <strong>use, modify, and distribute</strong> the software.</li> <li>If you modify and deploy this software (even as a web service), you <strong>must share your modifications</strong> under the same AGPL-3.0-or-later license.</li> <li>This ensures that improvements remain open-source and benefit the community.</li> </ul> <p>📖 Full license text: <a href="https://www.gnu.org/licenses/agpl-3.0.en.html">GNU AGPL v3.0</a></p> <div> <pre> </pre> </div>
Linked collectors and determiners for: NZCS Brownsberg Nature Park Monitoring.
Natural history specimen data linked to collectors and determiners held within, "NZCS Brownsberg Nature Park Monitoring". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/c8e6673d-a18c-4479-be1a-c795e759abfe">https://bionomia.net/dataset/c8e6673d-a18c-4479-be1a-c795e759abfe</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/c8e6673d-a18c-4479-be1a-c795e759abfe">https://gbif.org/dataset/c8e6673d-a18c-4479-be1a-c795e759abfe</a>. Formatted as a Frictionless Data package.
[Data] Recent consumer OLED monitors can be suitable for vision science
<p>Monitor data collected using X-Rite i1Display Colorimeter and X-Rite i1Pro3 Spectrophotometer. 4 datasets for 2 OLED monitors (Asus, Samsung), 1 LED projector (VPixx PROPixx), and 1 LCD monitor (LG UltraGear).</p> <p>Each dataset contains luminance measurements of R, G, B channels and grayscale, pixel signal responses and waveforms that were recorded using OSRTT PRO CS (photodiodes). Luminance measurements include ramps, two channels saturated (OCS), filling factor (Average Pixel Level), and uniformity measurements.</p>
Linked collectors and determiners for: Checklist of pioneer benthic taxa found on Autonomous Reef Monitoring Structures (ARMS) in Terra Nova Bay (Ross Sea, Antarctica).
Natural history specimen data linked to collectors and determiners held within, "Checklist of pioneer benthic taxa found on Autonomous Reef Monitoring Structures (ARMS) in Terra Nova Bay (Ross Sea, Antarctica)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/f024a3d3-7bb2-4985-be57-d890936769ff">https://bionomia.net/dataset/f024a3d3-7bb2-4985-be57-d890936769ff</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/f024a3d3-7bb2-4985-be57-d890936769ff">https://gbif.org/dataset/f024a3d3-7bb2-4985-be57-d890936769ff</a>. Formatted as a Frictionless Data package.
Long-term water quality monitoring data of Shenzhen Bay
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Comparing Spatial and Temporal Variabilities between the Vaisala AQT530 Monitor and Reference Measurements
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Guildford Compact Neutron Monitor Data 1hr 2021-2024
<p>Data from the "compact neutron monitor" at Guildford, Surrey, UK. The data is 1 hour resolution from February 02 2021 until September 16 2024. The variables are:</p> <ul> <li>DATE_TIME: UTC time corresponding to the start of the recording interval</li> <li>CTS_X: uncorrected counts from detector X</li> <li>SAMPLE_LEN_X: sample length for detector in number of seconds</li> <li>TA: atmospheric temperature in degrees celsius</li> <li>PA: atmospheric pressure in hPa</li> <li>RH: relative humidity in %</li> <li>CTS_X_CORR: pressure-corrected counts from dtetector X</li> </ul>
Monitoring Spatiotemporal Seismic Velocity Changes Using Seismic Interferometry and Distributed Acoustic Sensing in Mexico City
<h2>Cross-Correlation Functions (CCFs) Data Files</h2> <p>The dataset consists of four zipped files:</p> <ul> <li> <p><strong>cc_25hz_das1_Freq_0.40-1.20hz.zip</strong></p> <ul> <li> <p>Contains CCFs using DAS fiber-1, sampled at 25 Hz, for the frequency range of 0.40–1.20 Hz.</p> </li> </ul> </li> <li> <p><strong>cc_25hz_das1_Freq_1.20-3.60hz.zip</strong></p> <ul> <li>Contains CCFs using DAS fiber-1, sampled at 25 Hz, for the frequency range of 1.20–3.60 Hz.</li> </ul> </li> <li> <p><strong>cc_25hz_das2_Freq_0.40-1.20hz.zip</strong></p> <ul> <li> <p>Contains CCFs using DAS fiber-2, sampled at 25 Hz, for the frequency range of 0.40–1.20 Hz.</p> </li> </ul> </li> <li> <p><strong>cc_25hz_das2_Freq_1.20-3.60hz.zip</strong></p> <ul> <li>Contains CCFs using DAS fiber-2, sampled at 25 Hz, for the frequency range of 1.20–3.60 Hz.</li> </ul> </li> </ul> <h4> </h4>
IMS Monitoring & Evaluation (M&E) Pre-Coded Excel Sheet
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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.