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Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2020/2021 – France
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: French Agency for Food, Environmental and Occupational Health & Safety</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2020/2021 – Austria
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: Austrian Agency for Health and Food Safety</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2020/2021 – Lithuania
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: National Food and Veterinary Risk Assessment Institute</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2020/2021 – Romania
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: Institute of Hygiene and Veterinary Public Heath and Institute for Diagnosis and Animal Health</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2020/2021 – Germany
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods, as well as the voluntary reporting of and Whole Genome Sequencing (WGS) data are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: Federal Institute for Risk Assessment</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2020/2021 – Estonia
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: Veterinary and Food Board</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2020/2021 – Finland
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods, as well as the voluntary reporting of and Whole Genome Sequencing (WGS) data are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: Finnish Food Authority</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2020/2021 – Luxembourg
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: Administration des services vétérinaires</p>
Intersection Monitoring: A dataset for vehicle detection using an infrastructure camera including ground truth vehicle localization
<p><strong>Intersection Monitoring: A dataset for vehicle detection using an infrastructure camera including ground truth vehicle localization.</strong></p> <p>This dataset contains images from a infrastructure camera monitoring an intersection and the ground-truth information for a vehicle crossing it from different directions. The data is aimed to develop and improve image-based vehicle detection algorithms.</p> <p>The dataset includes two different recordings with the same structure. For each of them, the sequence of images is provided in PNG format, along with the ground truth of the vehicle. The ground truth data was captured using a high-precision GNSS receiver installed on the test vehicle, fused with in-vehicle sensors using an Extended Kalman Filter (EKF) .</p> <p><strong>Time considerations</strong></p> <p>The camera and GNSS receiver clocks were synchronized before each test to have the same time base.</p> <p>Each test last about 140 seconds.</p> <p><strong>Image files</strong></p> <p>Image files can be found on the <strong>img/</strong> folder within for each test. The camera was configured to record images at 25 fps:</p> <ul> <li>Test 1: 3704 files</li> <li>Test 2: 3426 files</li> </ul> <p><strong>Vehicle localization ground truth</strong></p> <p>The test vehicle is a prototype of Autonomous Vehicle developed by the <a href="https://autopia.car.upm-csic.es">AUTOPIA</a> research group at the <a href="https://car.upm-csic.es">Centre for Automation and Robotics</a> in Spain.</p> <p>Vehicle location mainly depends on a Trimble BX982 GNSS receiver using RTK inputs from a local station. However, the location algorithm applies an EKF for combining GNSS measurements with different onboard sensors providing yaw rate, longitudinal acceleration and speed, steering wheel position and speed, etc.</p> <p>For each test, the <strong>vehicle.csv</strong> file contains the vehicle information recorded at 20Hz. This file includes:</p> <ul> <li>UTM Time: Time using the format HHMMSSss: <ul> <li>HH: Hours</li> <li>MM: Minutes</li> <li>SS: Seconds</li> <li>ss: Fraction of seconds</li> </ul> </li> <li>UTM East: East coordinate of the GNSS antenna in the UTM frame, in meters.</li> <li>UTMNorth: North coordinate of the GNSS antenna in the UTM frame, in meters.</li> <li>Orientation: Yaw angle of the vehicle, measured from the East axis (x-axis).</li> <li>Speed: Vehicle speed in m/s</li> <li>Acceleration: Vehicle acceleration in m/s^2</li> </ul> <p><strong>Camera info</strong></p> <p>The file <strong>camera_parameters.json</strong> includes all the information about the intrinsic and extrinsic parameters for the camera. The configuration stored on this file appplies to all tests.</p> <p>The camera used is an AXIS M1125 with variable focal length. It was installed in a communication tower near the intersection.</p> <p><strong>Vehicle info</strong></p> <p>The file <strong>vehicle_parameters.json</strong> includes information about vehicle dimensions and antenna location. The GNSS antenna is installed near the rear axle of the vehicle, in the middle part of the vehicle. The configuration stored on this file appplies to all tests.</p> <p><strong>Tools</strong></p> <p>Some MATLAB tools can be found in <a href="https://github.com/autopia-car/datasets-tools-intersection-monitoring">https://github.com/autopia-car/datasets-tools-intersection-monitoring</a></p> <p><strong>Disclaimer</strong></p> <p>That the dataset comes "AS IS", without express or implied warranty and/or any liability exceeding mandatory statutory obligations. This especially applies to any obligations of care or indemnification in connection with the dataset. The dataset was created for our research purposes only and no quality assessment was done for the usage in products of any kind. We can therefore not guarantee for the correctness, completeness or reliability of the provided data set.</p>
Supporting information for "Incoming Neutron Flux Corrections for Cosmic-ray Soil and Snow Sensors Using the Global Neutron Monitor Network"
<p>This dataset includes 2 files that represent supporting information for McJannet, D and Desilets, D (Submitted 2023)"Incoming Neutron Flux Corrections for Cosmic-ray Soil and Snow Sensors Using the Global Neutron Monitor Network" Water Resources Research.</p> <p>File 1 - Supporting Information 1 - Example calculation: Excel sheet showing demonstration calaculations using the neutron intensity correction described in the paper</p> <p>File 2 - Supporting information 2 - List of neutron moniotr stations used in the paper and acknowledgment of their contribtuion</p>
Dataset for "Characterization of rainwater infiltration within a controlled experiment by self-potential monitoring and modeling"
<p>This dataset provides the raw experimental data for the manuscript "Characterization of rainwater infiltration within a controlled experiment by self-potential monitoring and modeling". By imposing rainfall, we monitored the self-potential, volumetric water content, and temperature at different depths of a soil-column model within a water infiltration process. </p> <p> </p>
Crypto Price Monitoring Dataset for On-chain Derivatives Research
<p># Crypto Price Monitoring Repository</p> <p>This repository contains two CSV data files that were created to support the research titled "Price Arbitrage for DeFi Derivatives." This research is to be presented at the IEEE International Conference on Blockchain and Cryptocurrencies, taking place on 5th May 2023 in Dubai, UAE. The data files include monitoring prices for various crypto assets from several sources. The data files are structured with five columns, providing information about the symbol, unified symbol, time, price, and source of the price.</p> <p>## Data Files</p> <p>There are two CSV data files in this repository (one for each date):</p> <p>1. `Pricemon_results_2022_11_01.csv`<br> 2. `Pricemon_results_2022_11_08.csv`</p> <p>## Data Format</p> <p>Both data files have the same format and structure, with the following five columns:</p> <p>1. `symbol`: The trading symbol for the crypto asset (e.g., BTC, ETH).<br> 2. `unified_symbol`: A standardized symbol used across different platforms.<br> 3. `time`: Timestamp for when the price data was recorded (in UTC format).<br> 4. `price`: The price of the crypto asset at the given time (in USD).<br> 5. `source`: The name of the price source for the data.</p> <p>## Price Sources</p> <p>The `source` column in the data files refers to the provider of the price data for each record. The sources include:</p> <p>- `chainlink`: Chainlink Price Oracle<br> - `mycellium`: Built-in oracle of the Mycellium platform<br> - `bitfinex`: Bitfinex cryptocurrency exchange<br> - `ftx`: FTX cryptocurrency exchange<br> - `binance`: Binance cryptocurrency exchange</p> <p>## Usage</p> <p>You can use these data files for various purposes, such as analyzing price discrepancies across different sources, identifying trends, or developing trading algorithms. To use the data, simply import the CSV files into your preferred data processing or analysis tool.</p> <p>### Example</p> <p>Here's an example of how you can read and display the data using Python and the pandas library:</p> <p> import pandas as pd<br> <br> # Read the data from CSV file<br> data = pd.read_csv('Pricemon_results_2022_11_01.csv')<br> <br> # Display the first 5 rows of the data<br> print(data.head())` </p> <p> </p> <p>## Acknowledgements</p> <p>These datasets were recorded and supported by <a href="https://datamint.ai">Datamint</a> company (value-added on-chain data provider) and its team.</p> <p><br> ## Contributing</p> <p>If you have any suggestions or find any issues with the data, please feel free to contact authors.</p>
Data from: Towards automated ethogramming: Cognitively-inspired event segmentation for streaming wildlife video monitoring
<p><span>Our dataset, Nest Monitoring of the Kagu, consists of around ten days (253 hours) of continuous monitoring sampled at 25 frames per second. Our proposed dataset aims to facilitate computer vision research that relates to event detection and localization. We fully annotated the entire dataset (23M frames) with spatial localization labels in the form of a tight bounding box. Additionally, we provide temporal event segmentation labels of five unique bird activities: Feeding, Pushing leaves, Throwing leaves, Walk-In, and Walk-Out. The feeding event represents the period of time when the birds feed the chick. The nest-building events (pushing/throwing leaves) occur when the birds work on the nest during incubation. Pushing leaves is a nest-building behavior during which the birds form a crater by pushing leaves with their legs toward the edges of the nest while sitting on the nest. Throwing leaves is another nest-building behavior during which the birds throw leaves with the bill towards the nest while being, most of the time, outside the nest. Walk-in and walkout events represent the transitioning events from an empty nest to incubation or brooding, and vice versa. We also provide five additional labels that are based on time-of-day and lighting conditions: Day, Night, Sunrise, Sunset, and Shadows. In our manuscript, we provide a baseline approach that detects events and spatially localizes the bird in each frame using an attention mechanism. Our approach does not require any labels and uses a predictive deep learning architecture that is inspired by cognitive psychology studies, specifically, Event Segmentation Theory (EST). We split the dataset such that the first two days are used for validation, and performance evaluation is done on the last eight days.</span></p>
King's College Cambridge wildflower meadow monitoring data: biodiversity, climate change and society
<p class="MsoNormal">The biodiversity and climate crises are critical challenges of this century. Wildflower meadows in urban areas could provide important nature-based solutions, addressing the biodiversity and climate crises jointly, and benefitting society in the process. King's College Cambridge (England, UK) established a wildflower meadow over a portion of its iconic Back Lawn in 2019, replacing a fine lawn first laid in 1772.</p> <p class="MsoNormal">We used biodiversity surveys, Wilcoxon signed rank, and ANOVA models to compare species richness, abundance, and composition of plants, spiders, bugs, bats, and nematodes supported by the meadow, and remaining lawn, over three years. We estimated the climate change impact of meadow vs lawn from maintenance emissions, soil carbon sequestration, and reflectance effect. We surveyed members of the university to quantify the societal benefits of, and attitudes towards, increased meadow planting on the collegiate university estate.</p> <p class="MsoNormal">In spite of its small size (0.36 ha), the meadow supported approximately three times more plant species, three times more spider and bug species and individuals, and bats were recorded three times more often over the meadow than the remaining lawn. Terrestrial invertebrate biomass was 25 times higher in the meadow compared with the lawn. Fourteen species with conservation designations were recorded on the meadow (six for lawn), alongside meadow specialist species.</p> <p class="MsoNormal">Reduced maintenance and fertilising associated with meadow reduced emissions by an estimated 1.36 Mg CO<sub>2</sub>-e per hectare per year compared with lawn. Relative reflectance increased by 25-34% for meadow relative to lawn. Soil carbon stocks did not differ between meadow and lawn.</p> <p class="MsoNormal">Respondents thought meadows provided greater aesthetic, educational, and mental well-being services than lawns. In open responses, lawns were associated with undesirable elitism and social exclusion (most colleges in Cambridge restrict lawn access to senior members of the college), and respondents proved overwhelmingly in favour of meadow planting in place of lawns on the collegiate university estate.</p> <p class="MsoNormal">This study demonstrates the substantial benefits of small urban meadows for local biodiversity, cultural ecosystem services, and climate change mitigation, supplied at a lower cost than maintaining conventional lawn.</p>
Data from: Monitoring temporal and spatial trends of illegal and legal fishing in Canada's marine conservation areas using vessel tracking datasets
<p>Expansion of marine conservation areas (CA) necessitates resource-efficient and achievable strategies for monitoring and evaluation of ongoing fishing activity at national levels. To demonstrate and explore such a strategy, we conducted the first extensive analysis of fishing activity within Canada's static, geographically defined marine CAs with fishing regulations (n = 264 areas). We used eight years of Automatic Identification System data to estimate fishing effort across three oceans and conducted temporal and spatial comparisons specific to each CA's regulations and enactment date. We addressed questions on CA effectiveness, fishing displacement, fishing the line behavior, and relationships between fishing activity and spatial CA attributes. We estimated 22,000 hours of fishing activity within CAs after enactments, 22% of which was identified as illegal. CA effectiveness appeared to be lowest for Atlantic CAs based on illegal fishing effort density within CAs. Fishing displacement and fishing the line was generally not apparent as buffer areas around CAs tended to already have higher fishing effort prior to enactments. CA effectiveness and responses to CAs varied considerably, as was visualized using timeseries plots and maps developed for each CA. Our evaluation of a nation's full suite of CAs provides managers with a foundation and approach for continued monitoring and reporting.</p>
Electrical Impedance Tomography (EIT) measurement monitoring the respiratory status
<p>This dataset was used in a study titled "Structural priors represented by discrete cosine transform improve EIT functional imaging" which is currently pending publication. The dataset consists of Electrical Impedance Tomography (EIT) measurements that were collected during the monitoring of respiratory status in tested subjects. The data is stored as .get files, which can be imported into the Matlab® workspace using the codes provided on GitHub at https://github.com/rongqing-chen/DCT-EIT.</p> <p>Using the dataset requires the citation of the following publication:</p> <ul> <li>Lovas A, Chen R, Molnár T, Benyó B, Szlávecz Á, Hawchar F, et al. Differentiating Phenotypes of Coronavirus Disease-2019 Pneumonia by Electric Impedance Tomography. Frontiers in Medicine. 2022;9. doi: 10.3389/fmed.2022.747570</li> <li>R. Chen, S. Krueger-Ziolek, A. Lovas, B. Benyó, S.J. Rupitsch, K. Moeller (2023) Structural priors represented by discrete cosine transform improve EIT functional imaging. PLoS ONE 18(5): e0285619. 10.1371/journal.pone.0285619</li> </ul>
Long-term soil erosion monitoring in China using the RUSLE model based on Google Earth Engine
<p>This dataset contains soil erosion maps in China at 500m resolution from 2010 to 2020. The dataset is stored in GeoTif format with the unit of t/(km<sup>2 </sup>· a).</p>
What Do Firms Say in Reporting on Impacts of Climate Change? An Approach to Monitoring ESG Actions and Environmental Policy
<p>This paper focuses on two research questions arising from the 2010 U.S. Securities and Exchange Commission (SEC) Advisory on climate change reporting: (1) How does the discussion of climate change in SEC filings change after the Advisory? and (2) What are firms talking about when they talk about climate change?<br> Findings were obtained from the 218,000 10-K filings to the SEC during the 2000--2019 period. The study develops and applies text mining methodology based on extracting information from the ``semantic associates'' in the ``neighborhoods'' of indicative terms. On (1) it finds that climate change-related reporting does increase substantially after the SEC guidance. On (2) a nuanced picture emerges. Firms with comparatively larger transition risks tend to discuss climate change comparatively more, focusing on regulation-related topics. Firms exposed to the physical risks of climate change tend to discuss climate change somewhat less, focusing on meteorological topics. The results enrich our understanding regarding environmental policies and firms' behaviors regarding climate change. Theoretical and practical implications are provided.</p>
Automated processing of aerial imagery for geohazards monitoring: Results from Fagradalsfjall eruption, SW Iceland, August 2022
<p><strong>1-</strong> <strong>Dataset Summary</strong><br> Here we present a dataset of DEMs (Digital Elevation Models), orthomosaics, and lava area outlines for the August 2022 eruption at Fagradalsfjall, SW Iceland. The dataset consists of: (1) five aerial surveys collected over the course of the August 2022 Fagradalsfjall eruption, (2) one survey carried out on 14 August 2022 using Pléiades satellite stereo images, and (3) a larger aerial survey, covering the 2021 and 2022 eruption sites in late September 2022 after the volcanic activity concluded.</p> <p><strong>2- Background</strong></p> <p>The volcano at Fagradalsfjall, SW-Iceland, began erupting on 3 August 2022 at 13:20 following 10 months of quiescence. As part of the response plan, a series of photogrammetric surveys were conducted in rapid, operational mode throughout the duration of the eruption. Subsequent production of data products for natural hazards monitoring (lava maps, lava volumes, effusion rates) were calculated within hours and reported to the Icelandic Civil Defense, following a similar approach that described in Pedersen et al., 2022a and in Gouhier et al., 2022. At the start of the 2022 eruption, GCPs had not yet been placed around the new fissure, but reference data (orthomosaics and DEMs) which had been georeferenced using targets measured with differential GNSS existed of the eruption site from September 2021 from Pedersen et al. (2022b) were available to use as a reference in the new workflow instead of GCPs. Due to the urgent need from authorities for information about the new eruption, a processing method that avoids the time-consuming task of manual GCP selection using a reference image for georeferencing was preferable in this instance. Besides the acquisition of aerial photographs, the CIEST2 initiative was also re-activated to collect Pléiades stereo images in emergency mode (Gouhier et al., 2022).</p> <p><strong>3 – Overview of data collection</strong></p> <p>Table 1 contains the overview of the surveys collected and presented in this repository.</p> <p> Table 1. Summary of surveys included in this dataset, by survey date.</p> <table align="center"> <tbody> <tr> <td> <p><strong>Date & Time</strong><br> <strong>YYYYMMDD HH:MM</strong></p> </td> <td> <p><strong>Sensor</strong></p> </td> <td> <p><strong>Platform</strong></p> </td> <td> <p><strong>Flight alt.</strong><br> <strong>(m asl)</strong></p> </td> <td> <p><strong>Images</strong></p> </td> <td> <p><strong>Surveyed</strong><br> <strong>km<sup>2</sup></strong></p> </td> </tr> <tr> <td> <p>20220803 17:05</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203*</p> </td> <td> <p>~ 850</p> </td> <td> <p>46</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>20220804 11:00</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203</p> </td> <td> <p>~ 2100</p> </td> <td> <p>32</p> </td> <td> <p>35</p> </td> </tr> <tr> <td> <p>20220813 09:00</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203</p> </td> <td> <p>~ 750</p> </td> <td> <p>123</p> </td> <td> <p>9</p> </td> </tr> <tr> <td> <p>20220814 13:00</p> </td> <td> <p>Pléiades</p> </td> <td> <p>PHR1B</p> </td> <td> <p>n/a</p> </td> <td> <p>2</p> </td> <td> <p>14</p> </td> </tr> <tr> <td> <p>20220815 08:15</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203</p> </td> <td> <p>2100</p> </td> <td> <p>20</p> </td> <td> <p>23</p> </td> </tr> <tr> <td> <p>20220816 10:06</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203</p> </td> <td> <p>2100</p> </td> <td> <p>19</p> </td> <td> <p>26</p> </td> </tr> <tr> <td> <p>20220926 12:00</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-BMW**</p> </td> <td> <p>2100</p> </td> <td> <p>~20</p> </td> <td> <p>18</p> </td> </tr> </tbody> </table> <p>* TF-203: Savannah S aircraft</p> <p>** TF-BMW: Vulcanair P68 Observer 2 aircraft, operated by Garðaflug ehf.</p> <p><strong>4- Methods</strong></p> <p><strong>4.1 Processing of the aerial photographs from 3-16 Aug 2022</strong><br> Throughout the eruption, aerial surveys were conducted using a Hasselblad A6D 100 MP camera with 35 mm focal lens, from a height of 750 – 2,100 m above ground over the active lava field from an ultralight aircraft with a window in the bottom to allow for vertical photos to be taken (see supplement of Pedersen et al., 2022a for details and images of the setup). The camera was manually triggered to give ~70% overlap, and approximate flight lines were prepared beforehand for use with a handheld GPS during the flight to give ~30 % side overlap.<br> <br> An automated processing pipeline was created in python, which leverages tools from the Ames Stereo Pipeline (ASP, Shean et al., 2016) and Agisoft Metashape stand-alone Python API (v. 1.8.4). The processing and georeferencing of the aerial data were done in three steps, with all steps being automated except for the digitization of lava outlines. First, using a very high-resolution reference orthomosaic and DEM created in September 2021 and georeferenced with ground control points (Pedersen et al., 2022b), interest points (IPs) in each image were matched with the reference dataset, using the ASP routine <em>ipfind. </em>This created GCPs for each image over stable terrain. Second, hillshades were created from both the reference DEM and the source dataset DEM and matches in IPs were found in both, creating a second round of ground control points to refine the georeferencing of the entire block. Finally, the alignment of the source DEM was refined using the <em>dem_align</em> (demcoreg) protocol from Shean et al. (2016) by applying a bulk linear shift in X, Y and Z which minimizes the vertical difference in stable terrain between the source and reference DEM.</p> <p><strong>4.2 Processing of the Pléiades stereo images</strong><br> The Pléiades stereo images were processed using the Ames Stereo Pipeline, using the general workflow of <em>mapproject </em>and <em>parallel_stereo </em>(e.g., Deschamps-Berger et al., 2020). The <em>parallel_stereo </em>routine used default arguments, plus the following arguments:</p> <p><em>--stereo-algorithm asp_mgm -t rpcmaprpc --corr-seed-mode 3 --corr-max-levels 2 --cost-mode 3 --subpixel-mode 9 --corr-kernel 7 7 --subpixel-kernel 15 15</em></p> <p>We used the DEM from 4 Aug 2022 as the reference for <em>mapproject </em>and for the final DEM co-registration applied to the produced Pléiades DEM.</p> <p><strong>4.3 Processing of the 26 September 2022 dataset</strong><br> The survey from 26 September 2022 was collected and processed using direct georeferencing from an on-board GPS antenna. The final alignment of the block was refined using the dem_align (demcoreg) protocol from Shean et al. (2016) by applying a bulk linear shift in X, Y and Z which minimizes the vertical difference in stable terrain between the source and reference DEM. Because this survey covered a much larger area, the reference DEM for the final coregistration was the <a href="https://atlas.lmi.is/mapview/?application=DEM">ÍslandsDEM </a>v.1.0 (Landmælingar Íslands, 2022).</p> <p><strong>4.4. Maps of the lava outlines, lava thickness, lava volume, Time Average Effusion Rate (TADR)</strong><br> For each survey, a differential DEM (dDEM) showing elevation changes since the 2021 eruption was created by subtracting the reference DEM (ÍslandsDEM v.1.0, which includes the post-eruption DEM from Pedersen et al., 2022a) from the source DEM. Lava outlines, lava thickness lava volume, TADR and uncertainties were calculated using the methods described in Pedersen et al., 2022a. Table 2 summarizes calculations from this dataset.</p> <p> </p> <p>Table 2. Summary of survey results calculated from August 2022 Fagradalsfjall eruption DEMs and orthomosaics.</p> <table align="center"> <thead> <tr> <th> <p><strong> Date Start</strong></p> </th> <th> <p><strong>Date End</strong></p> </th> <th> <p><strong>Time </strong></p> <p><strong>Difference</strong></p> </th> <th> <p><strong>Lava </strong></p> <p><strong>Area<sup>*</sup> End </strong></p> <p><strong>(km<sup>2</sup>)</strong></p> </th> <th> <p><strong>dh<sup>**</sup> </strong></p> <p><strong>(m)</strong></p> </th> <th> <p><strong>Volume<sup>+</sup></strong></p> <p><strong>End<br> (1e+6 m<sup>3</sup>)</strong></p> </th> <th> <p><strong>TADR<sup>++</sup><br> (m<sup>3</sup>/s)</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>20220803<br> 13:20</p> </td> <td> <p>20220803<br> 17:05</p> </td> <td> <p>0d 03h 45m</p> </td> <td> <p>0.07</p> </td> <td> <p>5.88</p> </td> <td> <p>0.43</p> <p>± 0.03</p> </td> <td> <p>32.1</p> <p>± 1.5</p> </td> </tr> <tr> <td> <p>20220803<br> 17:05</p> </td> <td> <p>20220804<br> 11:00</p> </td> <td> <p>0d 21h 40m</p> </td> <td> <p>0.14</p> </td> <td> <p>11.13 </p> </td> <td> <p>1.57</p> <p>± 0.05</p> </td> <td> <p>17.7</p> <p>± 0.8</p> </td> </tr> <tr> <td> <p>20220804<br> 11:00</p> </td> <td> <p>20220813<br> 09:00</p> </td> <td> <p>8d 22h 00m</p> </td> <td> <p>1.27<sup>#</sup></p> </td> <td> <p>6.90</p> </td> <td> <p>10.33</p> <p>± 0.6</p> </td> <td> <p>11.4</p> <p>± 0.7</p> </td> </tr> <tr> <td> <p>20220813<br> 13:08</p> </td> <td> <p>20220814<br> 13:00</p> </td> <td> <p>0d 23h 52m</p> </td> <td> <p>1.24</p> </td> <td> <p>7.28</p> </td> <td> <p>10.62</p> <p>± 0.70</p> </td> <td> <p>2.8</p> <p>± 0.8</p> </td> </tr> <tr> <td> <p>20220814<br> 13:00</p> </td> <td> <p>20220815<br> 08:15</p> </td> <td> <p>0d 19h 15m</p> </td> <td> <p>1.26</p> </td> <td> <p>7.46</p> </td> <td> <p>10.99</p> <p>± 0.55</p> </td> <td> <p>4.1</p> <p>± 0.8</p> </td> </tr> <tr> <td> <p>20220815<br> 08:15</p> </td> <td> <p>20220816<br> 10:16</p> </td> <td> <p>1d 2h 01m</p> </td> <td> <p>1.28</p> </td> <td> <p>7.49</p> </td> <td> <p>11.13</p> <p>± 0.53</p> </td> <td> <p>2.0</p> <p>± 0.7</p> </td> </tr> <tr> <td> <p>20220816<br> 10:16</p> </td> <td> <p>20220821<br> 06:00<sup>##</sup></p> </td> <td> <p>4d 19h 44m</p> </td> <td> <p>1.28</p> </td> <td> <p>7.69</p> </td> <td> <p>11.39</p> <p>± 0.44</p> </td> <td> <p>0.653</p> <p>± 0.10</p> </td> </tr> </tbody> </table> <p><sup>*</sup>Total area of the lava field since 2022-08-03 before activity started.</p> <p><sup>**</sup>dh end is the mean thickness of the lava flow-field in the end of the given period.</p> <p><sup>+</sup>Volume erupted since 2022-08-03 before activity started.</p> <p><sup>++</sup>Time-averaged discharge rate for the given period</p> <p><sup>#</sup>Extrapolated value. Survey does not cover entire active lava area.</p> <p><sup>##</sup>End Time: 21 August 2022, 6:00. This time deduced from field observations from members of the Institute of Earth Sciences, University of Iceland. Values calculated from 26 September 2022 dataset.</p> <p>Figures and visual summaries of the processing method, resulting lava volumes, and uncertainties can be found in this poster: <a href="https://ftp.lmi.is/stm/Sydney/Fagradalsfjall_Aug2022/poster_faf_automated_proc2_srg_2022.pdf">Fagradalsfjall August 2022</a>.<br> <br> Orthomosaics from this dataset are viewable online at: <a href="https://atlas.lmi.is/mapview/?application=umbrotasja">https://atlas.lmi.is/mapview/?application=umbrotasja</a></p> <p><strong>Data naming conventions:</strong></p> <ul> <li>Data type: DEM, ortho, outline, diffDEM, lavafree, lava</li> <li>Acquisition date: YYYYMMDD_HHMM</li> <li>Platform/Sensor for data collection: Pléiades (PLE), Hasselblad A6D from aircraft (A6D)</li> <li>Resolution: 2x2 m (DEMs) and 30x30 cm (Orthomosaics)</li> <li>Folders (by survey): YYYYMMDD_HHMM_platform (during eruption) or 'posteruption'_platform (sensors/platform: A6D or PLE)</li> </ul> <p><strong>Data Specifications:</strong></p> <ul> <li>Cartographic projection: ISN93 / Lambert 1993 (EPSG:3057, <a href="http://https:/epsg.io/3057">https://epsg.io/3057</a>)</li> <li>Origin of Elevation: meters above GRS80 ellipsoid (WGS84)</li> <li>Raster data format: GeoTIFF</li> <li>Raster compression system: ZSTD (<a href="http://facebook.github.io/zstd/">http://facebook.github.io/zstd/</a>)</li> <li>Vector data format: GeoPackage (<a href="https://www.geopackage.org/">https://www.geopackage.org/</a>)</li> <li>Pléiades dataset includes only DEMs because the Pléiades ortho imagery is for licensed use only. Please contact the authors for further information on this.</li> </ul>
Supplementary Files: "Improved baited remote underwater video (BRUV) for 24h re-al-time monitoring of surface and deep-sea marine species"
<p>In the supplementary material you will find: Figure S1: Photographs of the different parts of the innovative BRUV design; Video S1: Video footage of a bluntnose sixgill shark; Video S2: Types of markings in blue sharks bodies to identify individuals; Video S3: Other marine pelagic species.</p>
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Allen Brain Atlas
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OpenNeuro
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