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33 results for “change points”
Global forest cover loss tipping points leading to changing hydrologic responses
<p>This dataset describes the methods used to develop the results for study entitled: Global forest cover loss tipping points leading to changing hydrologic responses.</p> <p>EVENTS_List_45.docx is a table describing each deforestation event used for the study</p> <p>MATLAB Script 1: Plotting Hydrologic Sensitive Area against Tree cover loss every 10 % tree cover loss for all 45 events and adjusting Richard's curve function to obtain the parameters. This script uses EXCEL SHEET: HSiaresults.xlsx</p> <p>MATLAB Script 2: Computing the critical points of acceleration based on the Richards curve parameters. This script uses the parameters or results obtained in Script one.</p> <p>MATLAB Script 3: Plotting the climate and water yield direction against tree cover loss. This script used EXCEL SHEET: direction.xlsx</p>
Supplementary Datasets and Movies for the Paper "Mapping finite-fault earthquake slip using spatial correlation between seismicity and point-source Coulomb failure stress change"
<p>Supplementary Datasets and Movies for the Paper <br><strong>Mapping finite-fault earthquake slip using spatial correlation between seismicity and point-source Coulomb failure stress change </strong><br>by Anthony Lomax</p> <p>DOI: <a href="https://doi.org/10.48550/arXiv.2404.05437" target="_blank" rel="noopener">https://doi.org/10.48550/arXiv.2404.05437</a></p> <p> </p> <p><strong>Movie S1 Animation of the 2018, Mw 7.1 Anchorage, Alaska sequence and background seismicity 2014-2022.</strong> Relocated seismicity shown for: 2014 – 2018 mainshock (light blue), 2018 mainshock – 1 month after mainshock (green), 1 month after mainshock through 2022 (light orange); large black dot indicates the Mw 7.1 mainshock hypocenter. See figure caption in main paper for more details.</p> <p><strong>Movie S2 Animation of seismicity-stress, 3D finite-faulting potential slip results the 2018 Mw 7.1 Anchorage, Alaska earthquake sequence.</strong> The high-potential portion of the seismicity-stress finite-faulting field is shown in red for west-dipping reciever faults inferred from the first 1 day of aftershocks (blue dots) after the 2018 mainshock (large black dot). See figure caption in main paper for more details.</p> <p> </p> <p><strong>CSV (.csv) and NLL-Hypocenter (.hyp) format catalogs of NLL-SSST-coherence relocations used in this study:</strong></p> <p>Parkfield_2022_NLL-SSST-coherence_20231201A.csv<br>Parkfield_2022_NLL-SSST-coherence_20231201A.hyp</p> <p>AntelopeValley_2021_NLL-SSST-coherence_20231223A.csv<br>AntelopeValley_2021_NLL-SSST-coherence_20231223A.hyp</p> <p>Anchorage_2018_NLL-SSST-coherence_20231125A.csv<br>Anchorage_2018_NLL-SSST-coherence_20231125A.hyp</p> <p> </p>
Oriental Honey-Buzzards Dataset | Climate change leads to range contraction for the Oriental Honey-Buzzards: How to point out the future conservation strategies?
<p>This dataset contains raster data (.TIF) in probability and binary outputs of oriental honey-buzzards distribution within the wintering and breeding areas under changing climate.</p> <p><strong>File Size</strong>: ~184 MB (13.8 MB in compressed ZIP file)</p> <p><strong>Format File</strong>:</p> <p><em>ohb_A_B_C</em>.tif (.tfw; .XML; .dbf)</p> <p><strong>A</strong>: breeding or wintering</p> <p><strong>B</strong>: timepoint and scenario. e.g., 2050ssp5 or 2010ssp2</p> <p><strong>C</strong>: binary or probability outputs. e.g., bin or prob. <em>Note: for binary maps, value 0: non-suitable areas for OHB and value 1: suitable areas for OHB</em></p> <p>For further inquiries. Please contact: aryo_acondro@apps.ipb.ac.id</p>
Fig. 1. Occurrence points for 12 in Projected Climate Change Effects On Nuthatch Distribution And Diversity Across Asia
Fig. 1. Occurrence points for 12 Sitta species and one Tichodroma species used in this study. Sitta solangiae and S. victoriae each had fewer than 5 occurrence points and were excluded from the analysis.
Health Record Hiccups - 5526 real-world time series with change points labelled by crowd-sourced visual inspection
<p>5526 real-world time series with labels for the location of all abrupt changes in level, variability, trend, presence/absence of data points, and irregular outliers. The time series were produced from a range of electronic health record data extracts from a large UK hospital group. Values in each data field were aggregated by day/week/month, and numeric summary values calculated for each timepoint from the (often non-numeric) data by applying simple functions (e.g. number of values present, percentage of missing values, number of distinct values, median value). Labels were produced by visual inspection of time series plots from ~2000 volunteers, via the Health Record Hiccups project on the Zooniverse platform (https://www.zooniverse.org/projects/phuongquan/health-record-hiccups). Volunteers drew a vertical line on the image wherever they saw a change point (green line if they were certain, yellow line if they were unsure). Consensus labels per image were calculated using density based clustering with noise (R v3.6.3, dbscan v1.1-5), and converted back to a date.</p>
Supplementary Data and Code: Change Point Analysis to decode Economic Crisis Information
<p>Raw data, results and Python code of the corresponding publication "Efficient Multi-Change Point Analysis to decode Economic Crisis Information from the S&P500 Mean Market Correlation" (accepted in: Entropy; Section: Complexity; Special Issue: Complexity in Finance). The change point analysis can be performed using the <a href="https://anticpy.readthedocs.io/en/latest/">documented</a> Python package <a href="https://github.com/MartinHessler/antiCPy"><em>antiCPy</em></a>. Some further helpful Python scripts are provided here under a <em>GNU General Public License v3.0.</em></p> <p>In Data_Generation you can find a list of</p> <ol> <li>S&P500 companies which are considered in the analysis,</li> <li>a jupyter notebook to create the correlation time series.</li> </ol> <p>Data_Preprocessing contains the</p> <ol> <li>S&P500 mean market correlation Financial_Time_Series_Centered_Interval__42days.csv,</li> <li>the Python code to thin it,</li> <li>the thinned data saved as .npy file,</li> <li>the time scale is saved <ul> <li>as integer numbers in thinned_time_thinning40.npy,</li> <li>as datetime in TimeScale_FinancialData.npy.</li> </ul> </li> </ol> <p>In Change_Point_Analysis you find the following files:</p> <ol> <li>cp_probs...npy contain the joint probabilities of the corresponding change point configurations (The joint probabilities are saved corresponding to the order in which Python's <em>itertools.combinations() </em>creates the configurations. This holds also for the cp_probs_5_cps.npy for which the whole combinations array is not saved for memory reasons),</li> <li>cp_pdfs...npy contain the marginal probability density functions of the ordinal change point positions averaged over all configurations,</li> <li>cp_configs...npy contain the configurations,</li> <li>segment_fit...npy contain the segment fit data,</li> <li>segment_fit_variance...npy contain corresponding variances,</li> <li>In the case of five change points only the plotted 1st, 13th and 26th most probable configuration in config_ranking_CP1_5.npy for memory reasons.</li> <li>the data and results of figure 3 for each crisis event can be found in the corresponding directory's folders: <ul> <li>blue corresponds to the pre-crisis data segments,</li> <li>green corresponds to the data segments up to the green vertical dotted line,</li> <li>red corresponds to the longest data segments incorporating near and in-crisis data.</li> </ul> </li> </ol> <p> </p> <p> </p>
Change in marsh surface elevation measured with a Surface Elevation Table (SET) at control plots in a Spartina alterniflora-dominated salt marsh at Law's Point, Rowley River, Plum Island Ecosystem LTER, MA.
A Surface Elevation Table (SET) is used to measure changes in the elevation of the marsh platform at a Spartina alterniflora-dominated marsh on the Rowley River in the Plum Island Ecosystem (PIE) LTER site, MA.
Change in marsh surface elevation measured with a Surface Elevation Table (SET) at control plots in a Spartina patens-dominated salt marsh at Law's Point, Rowley River, Plum Island Ecosystem LTER, MA.
A Surface Elevation Table (SET) is used to measure changes in the elevation of the marsh platform at a Spartina patens-dominated marsh on the Rowley River in the Plum Island Ecosystem (PIE) LTER site, MA.
Data from "Projections of leaf turgor loss point shifts under future climate change scenarios" (Tordoni et al. 2022 Global Change Biology)
<p>The dataset includes four sheets representing the average turgor loss point (tlp) values at grid cell level (tlp_data) and the climatic variables and related climate change scenarios derived from the three models used in this study (HadGEM2-ES-RACMO22E, EC-EARTH_RACMO22E, EC-EARTH_CCLM4-8-17, respectively).</p> <p>The sheet "tlp_data" reports the cell ID (OGU) and the average tlp values for each taxonomic group considered in this study (gymnosperms, angiosperms, herbaceous and woody angiosperms). </p> <p>Each of the other three sheets reports the cell ID (OGU), coordinates of the cell centroid (Long, Lat) and a set of six climatic variables: 95<sup>th</sup> percentiles of average temperature (BIO1.95, °C), temperature seasonality (BIO4, °C), annual consecutive frost days where temperature was ≤ 0 °C (CFD.ann, n° days), annual consecutive dry days where precipitation was < 1 mm (CDD.ann, n° days), 5<sup>th</sup> percentiles of cumulate annual precipitation (BIO12.5, mm), and precipitation seasonality (BIO15, %). For each model, "hist" refers to historical data encompassing the period 1970-2005, whereas "RCP2.6" and "RCP8.5" reports the average value of future projections for the period 2080-2100 in two representative concentration pathway (RCP) scenarios (RCP2.6 and RCP8.5).</p> <p> </p>
Examining the Influence of Focal Point Location on Heat Transfer Dynamics in Phase Change Material: A Dataset Publication
<p>Dataset including experimental data from small-scale solar facade module that consists of several components: Fresnel lens; cone-shaped air gap; aerogel (semi-transparent) as insulation layer; PCM container. Variations of the experimental settings in test setups are different cone diameter (2, 3 and 4 cm) and insulation layer thickness is (3,5 and 7cm). Nine different compositions were tested in laboratory to evaluate the ompact of focal point location. The focal point has been described with three different values that reflect its location on the inner and outer surfaces and in the middle of the PCM container. The corresponding figures can be found in the paper The Effect of Fresnel Lens Focal Point Location on Heat Transfer in Phase Change Material (PCM) Enhanced Dynamic Solar Facade.</p> <p>This study has been supported by Fundamental and Applied Research project ‘Smart building EnVElope with solaR Energy STorage (EVEREST)’, project No. lzp-2019/1-0363, funded by the Latvian Council of Science.</p> <p> </p>
Stratification reference points for Luhansk region tree cover change (1996,2000,2013,2020)
<p><strong>Occupation of Environment: Long-Term Consequences of war in Ukraine on conservation of forested areas of Emerald Network in the Luhansk region - </strong>Generated sample points that reflect stratification type and landcover change for Luhansk region for tree cover change area estimation. The division into territories controlled by Ukraine and Russia made based on the demarcation line with consideration of the buffer zone, where Ukrainian institutions were disconnected due to the active warfare in purpose of separation of zones with interruption of Ukrainian environmental policies.</p>
The Turing Change Point Dataset
<p>This is the first complete release of the Turing Change Point Dataset, a dataset created specifically for the evaluation and development of change point detection algorithms. For more information, see: <a href="https://arxiv.org/abs/2003.06222">https://arxiv.org/abs/2003.06222</a></p>
Data for the "Deforestation could push Amazonia close to a tipping point under future climate change." paper
<p>Matlab datafiles for the output of the HYBRID-ICBL <span>coupled land surface-atmosphere single column model presented in the "Deforestation could push Amazonia close to a tipping point under future climate change"</span></p> <p><span>Values are daily mean values and their names are self explanatory as follows.</span></p>
Tipping points and multiple drivers in changing aquatic ecosystems: A review of experimental studies (Data)
<p>The ecological concepts of tipping points and multiple stressors are both important concepts for the management of natural ecosystems. However, very few studies have attempted to combined both concepts and evaluate tipping points in the context of multiple stressors. The data available here have been published in a literature review (Limnology & Oceanography). In this review we (1) develop a historical and current perspective of tipping point studies in terrestrial, freshwater and marine ecological systems; (2) portray the research effort in different freshwater and marine habitats; and (3) explore the results of experimental studies focusing on tipping points measured at the individual, communities, ecosystem level, as well as ecosystem functions and services in a context of single and multiples stressors.</p>
Immediate Stiffness Changes in Myofascial Trigger Points After Dry Needling
ClinicalTrials.gov study NCT04832074. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Skin Surface's Temperature Change When Auricular Acupuncture at Jaw Point and Tooth Point
ClinicalTrials.gov study NCT05164302. IPD Sharing: YES. Countries: 1. Publications: 4.
Changes in Lumbar Region Temperature During Auricular Acupressure at Kidney Point
ClinicalTrials.gov study NCT06169098. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Data from: Free-energy changes of bacteriorhodopsin point mutants measured by single-molecule force spectroscopy
Open the record for dataset details and reuse information.
BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection
<p>Artifacts for the paper titled <strong><em>BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection</em></strong>.</p> <p>This artifact repository contains 3 compressed folders, as follows: </p> <table> <tbody> <tr> <td><strong>File Name</strong></td> <td><strong>Benchmark System</strong></td> </tr> <tr> <td>fse-ob.zip</td> <td>Online Boutique</td> </tr> <tr> <td>fse-ss.zip</td> <td>Sock Shop</td> </tr> <tr> <td>fse-tt.zip</td> <td>Train Ticket</td> </tr> </tbody> </table> <p>Each zip file contains the collected data from the corresponding microservice benchmark systems (e.g., fse-ob.zip contains metrics data collected from the Online Boutique system). </p> <p><strong><strong>Data description</strong></strong></p> <p>To collect the metrics data, we deploy three benchmark microservice systems: Online Boutique, Sock Shop, and Train Ticket, on a Kubernetes cluster consisting of one master node and five worker nodes. Then, we deploy a monitoring system to monitor and collect resource-level and service-level metrics. To generate traffic, we use the load generators supplied by these systems and tailor them to explore all services with a load of 40-50 requests per second. Initially, we operate the applications normally to gather metrics data under normal conditions. Then, we inject faults into the running services. We execute into the designated container using kubectl exec. For CPU hog and memory leak, we use stress-ng to stress the container resource. For network delay and packet loss, we use tc (traffic control) to manipulate the traffic of the container. Specifically, we inject faults into five targeted services of Sock Shop (carts, catalogue, orders, payment, and user), five targeted services of Online Boutique (adservice, cartservice, checkoutservice, currencyservice, and productcatalogue), and five targeted services of Train Ticket (ts-auth-service, ts-order-service, ts-route-service, ts-train-service, ts-travel-service). For each combination of fault type and targeted service, we repeat the operation (i.e., fault injection and metrics data collection) five times, resulting in 100 failure cases for each benchmark microservice system.</p> <p><strong>Code</strong></p> <p>The code to reproduce the experimental results in the paper is available at <a href="https://github.com/phamquiluan/baro">https://github.com/phamquiluan/baro</a>.</p>
An Empirical Study on Analyzing the Factors that Affect the Triagement of Change Points
<p>An Empirical Study on Analyzing the Factors that Affect the Triagement of performance Change Points</p>
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