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177 results for “Early warnings”
Quantifying changes in fish population stability using statistical early warnings of regime shifts
This data package describes long-term trends in metrics describing population stability and used as statistical early warnings of regime shifts in 29 fish species that inhabit the San Francisco Bay-Delta in central California, USA. Metrics used in this study include spatial synchrony, temporal coefficient of variation (CV), and lag-1 temporal autocorrelation. Trends were measured using ordinary least squares linear regression. These derived data were developed from abundance (as CPUE) time series based on three long-term fish monitoring studies included in https://doi.org/10.6073/pasta/a29a6e674b0f8797e13fbc4b08b92e5b; the Fall Midwater Trawl Survey, Delta Juvenile Monitoring Program, and Bay Study. Selected data were from fall months (September to December) in 1980-2023, from midwater trawl and beach seine surveys for which sampling effort (e.g., tow volume) was recorded. Data on fish exceeding maximum length thresholds for age-0 fish were discarded, except for white sturgeon, where the maximum length threshold corresponded to approximately 10 years of age, the onset of reproductive maturity. Observations from different sampling stations were aggregated into 10 sub-regions (South San Francisco Bay, Central San Francisco Bay, San Pablo Bay, Napa River, Suisun Bay, Delta Confluence, South Delta, North Delta, San Joaquin River, Sacramento River, and midwater trawl samples and beach seine samples were considered separately because the methods sample distinct habitat types. Combinations of sub-region and sampling method were considered distinct spatial units. EWI metrics were measured in 5-year rolling windows to permit assessment of changes over time. The temporal CV and lag-1 autocorrelation were measured on individual spatial unit time series, ignoring windows with >1 year of missing data. The coefficient of variation divides the standard deviation by the mean. Lag-1 autocorrelation was measured as Pearson correlation. Spatial synchrony was measured across spatial un
Data and code of the article: "Early Warning Signals of the Termination of the African Humid Period(s)"
<p>Data and MATLAB Code of the article Trauth, M.H., Asrat, A., Fischer, M.L., Hopcroft, P.O., Foerster, V., Kaboth-Bahr, S., Kindermann, K., Lamb, H.F., Marwan, N., Maslin, M.A., Schaebitz, F., Valdes, P.J. (2024) Early Warning Signals of the Termination of the African Humid Period(s), Nature Communications, https://doi.org/10.1038/s41467-024-47921-1. The individual directories contain the data and the MATLAB code used to generate Fig. 1 and 2 and Supplementary Fig. 1 to 7 published with the article.</p>
Cascade project at North Temperate Lakes LTER - Daily data for key variables in whole lake experiments on early warnings of critical transitions, Paul and Peter Lakes, 2008-2011
Peter Lake's food web was altered by adding largemouth bass at a slow rate while monitoring key food web constituents including littoral minnow abundance indexed as catch per trap per hour, zooplankton biomass, and concentration of chlorophyll a. Paul Lake was manipulated and the same variables were measured there. In Peter Lake, we expected littoral catch of minnows to first increase as minnows moved into the littoral zone due to the threat of bass predation and then decrease due to bass predation. We expected zooplankton biomass to increase as minnows moved into the littoral zone. We expected chlorophyll to decrease due to increased grazing by zooplankton. We expected that variance and autocorrelation of chlorophyll would increase as the food web passed a critical transition. We expected that the time series in Paul Lake would represent the normal variability of an unmanipulated lake
Eco-evolutionary processes underlying early warning signals of population declines
<p>Datasets for the paper appearing in Journal of Animal ecology : "Eco-evolutionary processes underlying early warning signals of population declines". Also GitHub repository link :<a href="https://github.com/GauravKBaruah/ECO-EVO-EWS-DATA">https://github.com/GauravKBaruah/ECO-EVO-EWS-DATA</a></p>
Expansion of the early warning system for avian influenza in the EU to evaluate the risk of spillover from wild birds to poultry
<p>GIF 1: Animated map of the spatiotemporal variations of the estimated number of infectious wild birds between February 2023 and March 2024</p> <p>GIF 2: Animated map of the spatiotemporal variations of transmission rate from wild birds to indoor chicken between February 2023 and March 2024</p> <p>GIF 3: Animated map of the spatiotemporal variations of transmission rate from wild birds to outdoor chicken between February 2023 and March 2024</p> <p>GIF 4: Animated map of the spatiotemporal variations of transmission rate from wild birds to indoor duck between February 2023 and March 2024</p> <p>GIF 5: Animated map of the spatiotemporal variations of transmission rate from wild birds to outdoor duck between February 2023 and March 2024</p> <p>GIF 6: Animated map of the spatiotemporal variations of the probability of HPAI introduction into indoor chicken farms between February 2023 and March 2024</p> <p>GIF 7: Animated map of the spatiotemporal variations of the probability of HPAI introduction into outdoor chicken farms between February 2023 and March 2024</p> <p>GIF 8: Animated map of the spatiotemporal variations of the probability of HPAI introduction into indoor duck farms between February 2023 and March 2024</p> <p>GIF 9: Animated map of the spatiotemporal variations of the probability of HPAI introduction into outdoor duck farms between February 2023 and March 2024</p> <p>GIF 10: Animated map of the spatiotemporal variations of the probability of HPAI introduction into poultry farms between February 2023 and March 2024 (scenario A)</p> <p>GIF 11: Animated map of the spatiotemporal variations of the probability of HPAI introduction into poultry farms between February 2023 and March 2024 (scenario B)</p>
Extracted and Anonymised Qualitative Data on Students' Acceptance of an Early Warning System
<p>The data published in this record was adopted in the following study: </p> <p><em><strong>Exploring Higher Education students' experience with AI-powered educational tools: The case of an Early Warning System </strong></em></p> <p>The study analyses the students' experience of an early warning system developed at a fully online university. The study is based on 21 semi-structured interviews that yielded a corpus of 21,761 words, for which a mixed inductive and deductive codification approach was applied after thematic analysis. We focused on 11 themes, 52 subthemes, and 396 coded segments to perform content analysis. Our findings revealed that the students, primarily senior workers with a high-level academic self-efficacy, had little experience with this type of system and low expectations about it. However, a usage experience triggered interest and meaningful reflections on the mentioned tool. Nevertheless, a comparative analysis between disciplines related to Computer Science and Economics showed higher confidence and expectation about the system and artificial intelligence overall by the first group. These results highlight the relevance of supporting students' further experiences and understanding of artificial intelligence systems in education to accept them and mainly to participate in iterative development processes of such tools to achieve quality, relevance, and fairness.</p> <p>The three records attached as part of the dataset include:</p> <p>1- The General CodeTree with exemplar coding excerpts in Spanish<br> 2- Extract of transcriptions in English<br> 3- Full Report in Spanish as extracted from NVIVO, including the extracted codes for the synthesis (1,2) in blue, and the comments made by the two researchers engaged in the interrater agreement.<br> 4- General Content Analysis (Spreadsheet ODS)</p> <p> </p>
Integrated Machine Learning model in Early Urban Flooding Warning System - Data
<p>AI_DATA.npy - Inundation data (mm) generated from MIKE+ model that has been converted to numpy array</p> <p>INDEX.npy - The index where inundation is > 0 </p> <p>source.tif - Source tif image for creating map from ML models</p>
Fig. 2 in Dynamis borassi (Coleoptera: Curculionidae), a new potential pest to the palms (Arecaceae): an early warning for the palm producers
Fig. 2. Variable area transects used in the evaluation of the peach palm infestation by Dynamis borassi and Rhynchophorus palmarum in 33 production sites in Colombia. The size of each sampling rectangle is determined for the number of affected palms, as illustrated.
Fig. 4 in Dynamis borassi (Coleoptera: Curculionidae), a new potential pest to the palms (Arecaceae): an early warning for the palm producers
Fig. 4. Precipitation, weevil abundance, and mean of damaged inflorescences between Sep 2018 and Oct 2019. The precipitations (top panel) corresponding to mean daily rainfall (circle) and proportion of d with rain (triangles) were documented for Bajo Calima (solid line) and Sabaletas (dashed line). The abundance was represented with whiskers diagram for Dynamis borassi (white boxes) and Rhynchophorus palmarum (gray boxes). The asterisks are outliers. The damaged inflorescences were reported with rhombus.
Fig. 3 in Dynamis borassi (Coleoptera: Curculionidae), a new potential pest to the palms (Arecaceae): an early warning for the palm producers
Fig. 3. Types of damages of weevil-damaged peach palms in 32 localities of Colombia. Inflorescence with perforation (arrow), circular hole at the insertion points and weevil larva inside (squares) (a); external view of trunk perforated by larvae, with round perforation exactly at the spot where the inflorescence is attached to the stem (arrow) (b); internal feeding galleries with larvae (arrow) (c); toppled crown (d); percent of affected palms by locality (e).
Figure 3. Screenshot of the MQTT broker, publisher, and two subscribers.-Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology
<p>As it was mentioned above, IoT needs the appropriate lightweight protocols to transmit the<br> info because web-protocols (e.g. TCP) generate several times more traffic usually for IoT (e.g.<br> remote connection to the Arduino weather station). MQTT (Message Queuing Telemetry Transport)<br> and CoAP (Constrained Application Protocol) IoT protocols are mainly in use nowadays<br> (http://postscapes.com/internet-of-things-protocols). In this activity,Arduino Ethernet Shield and C#<br> console app are connected by MQTT Mosquitto open source software (http://mosquitto.org).Similar<br> work presented against https://iotguys.wordpress.com/2014/11/13/arduino-with-mqtt/.The activity<br> consists of the following steps:<br> 1. Download and installation of Mosquitto software gainst http://mosquitto.org/download/.<br> 2. Download and installation of the latest Arduino software against<br> http://arduino.cc/en/main/software.<br> 3. Development of the MQTT subscriber based on C programming language in Arduino<br> IDE.<br> 3. Development of the MQTT subscriber based on C# console app (laptop HP ProBook 650<br> G1 and Windows 10 are used) in Visual Studio.<br> Screen shot of the software is shown in Fig. 3.</p>
Figure 2. Screenshot of the Google Earth web-site's prototype on the visualization of heat/cold waves-Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology-
<p>A non-anticipative analog method consists of four main steps:<br> 1. Generation of the prediction rules.<br> 2. Analysis of the prediction rules. The rules with time slots, which are not concentrated at<br> the same frame, are excluded.<br> 3. Generation of possible extremes.<br> 4. Analysis of the generated possible extremes. The extremes with time slots, which do not<br> correspond to the time slots of the appropriate rules, are excluded.<br> The results of the heat/cold waves’ prediction from 2011 to 2014 at different locations<br> (places are selected randomly) are presented in Table 2.</p>
Figure 1. Azure management portal and VM with two Delphi desktop apps-Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology
<p><br> Nowadays, only D-Wave Systems Company produces commercially the 2nd generation<br> adiabatic quantum computer with up to 512 flux qubits (project code name ″Vesuvius″). They are<br> microscopic loops of niobium metal that are capable of quantum behavior at low temperatures.<br> Hence, electrical currents in the loops can flow in clockwise (+1) or counterclockwise (-1)<br> direction, or both, when in quantum superposition. Qubits are connected to neighbors according to<br> the topology of quantum processor. The hardware is controlled by a framework of Josephson<br> junctions that allow individual qubit values to be stored and read, and to influence the states of<br> neighboring qubits.</p>
Earthquake Early Warning Global Test Suite
<p>This is the companion data to the JGR paper "Quantifying the Value of<br> Real-time Geodetic Constraints on Earthquake Early Warning using a<br> Global Seismic and Geodetic Dataset" by Ruhl et al. It contains both<br> strong motion and GNSS displacement waveforms. There is one folder per<br> event, and for each there is an "accel" and a "disp" file containing<br> each kind of data. There is a .chan channel file with station metadata.</p> <p>Changes from Version 1.0:</p> <p>The timing of seismic waveforms for Cascadia001300 were delayed by 1<br> minute and this has been corrected to match the geodetic data and the<br> origin time.</p> <p>Four of the Japanese events were mistakenly in GPS time and have now<br> been corrected into UTC time to match the seismic data. Affected events<br> include Tohoku2011, Miyagi2011B, E.Fukushima2011, and Kumamoto2016.</p>
ProbFire: a probabilistic fire early warning system for Indonesia
<p>This repository holds ProbFire model input datasets and pre-trained model weights and feature scaling parameters. Model code and description is available at https://github.com/ToFEWSI/ProbFire.</p>
Dataset: An early warning system for EM follow-up of GW events
<p>Visit</p> <p>https://gstlal.docs.ligo.org/ewgw-data-release/index.html</p> <p>for a web version.</p>
Infrasonic Early Warning System for Explosive Eruptions
<p>The two datasets 2018JB015561-ds01 and 2018JB015561-ds02 provide data used to create most of the figures and to reach most of the findings of the manuscript <em>[Ripepe, M.; Marchetti, E.; Delle Donne, D.; Genco, R.; Innocenti, L.; Lacanna, G.; Valade, S. Infrasonic Early Warning System for Explosive Eruption. J. Geophys. Res. Solid Earth <strong>2018</strong>, 123, 9570–9585].</em></p> <p>2018JB015561-ds01 is a matlab (,mat) file containing a sample of infrasound detections (December, 2nd, 2013) between 09:00 and 24:00 UTC. This dataset is used to create part of Figure 3 of the manuscript. Infrasound detections are obtained from raw infrasound data as discussed in Section 4. Infrasound detections (evaluated every 5 seconds) are used to create the IP, which is evaluated every minute as discussed in detail in Section 5.</p> <p><strong>Data Set S1. </strong>2018JB015561-ds01: The file consists three variables: <em>t</em>, that corresponds to matlab time; <em>pr</em>, that corresponds to the acoustic pressure of the detection as recorded at the array; <em>az</em>, that corresponds to the infrasound back-azimuth and is used to limit the analysis to infrasound produced by the volcano. The data has a time stamp of 5 seconds.</p> <p>2018JB015561-ds02 is a matlab (.mat) file containing the infrasound parameter, between January, 1st, 2008 and December, 31st, 2016. The infrasound parameter is obtained from infrasound detections following the procedure described in Section 5, and used eventually to provide the automatic notification as described in section 6. This dataset is used to create Figure 5 of the manuscript.</p> <p><strong>Data Set S2. </strong>2018JB015561-ds02: It contains two variables: <em>t</em>, that corresponds to matlab time; <em>ip</em>, that corresponds to the infrasound parameter. The data has a time stamp of 1 minute.</p> <p>The movie shows an example of how the IP calculated with the ETN infrasound array is efficiently tracking the changes of eruptive activity at Etna and can be used to deliver an alert of ongoing volcanic activity.</p> <p><strong>Movie S1. </strong>2018JB015561-ms01: The movie shows the eruptive activity at Etna volcano recorded between 23:00 UTC of December, 3rd, 2015, and 12:00 UTC of December, 4th, 2015. It shows movies from a thermal camera synchronized with the corresponding values of seismic tremor and Infrasound Parameter. The Early Warning level is over imposed on the IP.</p> <p> </p>
Localizing Hydrological Drought Early Warning using In-Situ Groundwater Sensors - Veness et al. (2022) - Dataset
<p>This upload contains full input data and modelling scripts to support AGU WRR's 'Localizing Hydrological Drought Early Warning using In-situ Groundwater Sensors' (Veness et al., 2022).</p> <p>'WRR_Data_Extraction' contains input data and processing of these for model input.</p> <p>'WRR_Modelling_Methods' contains two folders. 'Groundwater Model & Calibration' provides the code for the modified AquiMod, formatted for input in to an automated calibration procedure (run time approx. 5 minutes with 10,000 runs). The 'Plotting Files' folder contains the code converting the groundwater levels from the automated calibration procedure to plots seen in the paper.</p> <p>These scripts require use of the data and functions within the 'data_and_functions' folder, which may require careful directory management and minor edits to the code to ensure the scripts can communicate.</p>
Data: Approaching a population level assessment of body size in pinnipeds using drones, an early warning of environmental degradation.
<p>Data and R sctipts for measuring harbour seal body sizes and estimating mass based on .shp files containing outlines. Associated with the manusctipt currently titled "Approaching a population level assessment of body size in pinnipeds using drones, an early warning of environmental degradation."</p> <p>1_Seal_Volume_Function.R: A function for the estimation of length, width, and ellipsoid volume of harbour seals from georeferenced polygons representing individual outlines 2_Polygon_Process.R: This script uses the curved_length_vol function (1_Seal_Volume_Function.R) to process a folder full of .shp file subfolders containing georeferenced polygons representing individual outlines and outputs a .csv with estimates of length, width, and ellipsoid volume for each individual. 3_Calibration.R: This script processes and calibrates summarized harbor seal measurements based on reference to known individuals</p> <p>CSV_Files: Folder containing data files</p> <p>Known_Seals.csv: True measurments of length and mass for known seals with derived estimates of 'true' width and volume. Drone based estimates of length, width, simple and complex volume for the same individuals, information on pose.</p> <p>measurments.csv: Drone based estimates of length, width, simple and complex volume for all individuals.</p> <p>Pup_growth.csv: Data on pup mass by age from Harding et al. 2005 (<a href="https://doi.org/10.1111/j.0269-8463.2005.00945.x" rel="nofollow">https://doi.org/10.1111/j.0269-8463.2005.00945.x</a>).</p> <p>Slottsskogen_Data.csv: True and drone based measurments for individual captive harbour seals taken on two seperate occasions.</p> <p>Summarised_Weights.csv: True measurments of length, girth, and mass for harbour seals.</p>
Data used in manuscript "High-resolution geophysical monitoring of moisture accumulation preceding slope movement – a path to improved early warning"
<p>Data used in the study titled "High-resolution geophysical monitoring of moisture accumulation preceding slope movement – a path to improved early warning" published in Environmental Research Letters</p>
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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)
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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.