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11 results for “trend detection”
A new merged dataset of global ocean chlorophyll-a concentration for better trend detection
<p>Chlorophyll-a concentration (Chla) is recognized as an essential climate variable and is one of the primary parameters of ocean-color satellite products. Ocean-color missions have accumulated continuous Chla data for over two decades since the launch of SeaWiFS in 1997. However, the on-orbit life of a single mission is about five to ten years. To build a dataset with a time span long enough to serve as a climate data record (CDR), it is necessary to merge the Chla data from multiple sensors. The European Space Agency has developed two sets of merged Chla products, namely GlobColour and OC-CCI, which have been widely used. Nonetheless, issues remain in the long-term trend analysis of these two datasets because the intermission differences in Chla have not been completely corrected. To obtain more accurate Chla trends in the global and various oceans, we produced a new dataset by merging Chla records from the Sea-viewing Wide Field-of-view Sensor, Medium-spectral Resolution Imaging Spectrometer, Moderate Resolution Imaging Spectroradiometer, Visible Infrared Imaging Radiometer Suite, and Ocean and Land Colour Instrument with intermission differences corrected in this work. The fitness of the dataset as a CDR was validated by using in situ Chla and comparing the trend estimates to the multi-annual variability of different satellite Chla records. </p>
Tables and data for "Downward Trend in Methane Detected in a Northern Colorado Oil and Gas Production Region Using AIRS Satellite Data"
<p>These are tables and data files for the paper "Downward Trend in Methane Detected in a Northern Colorado Oil and Gas Production Region Using AIRS Satellite Data" submitted to the Journal of Atmospheric Research: Atmospheres</p>
Imperfect detection in plant populations can cause misestimates of demographic rates and missed population trends: The case for Astragalus microcymbus Barneby
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Data from: Optimising monitoring for trend detection after 16 years of woodland-bird surveys
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Autism Detection Based on Eye Movement Sequences on the Web: A Scanpath Trend Analysis Approach
<p>We propose a novel approach to detect autism based on the eye-movement paths of users on the Web. This approach based on Scanpath Trend Analysis (STA) proposed by Eraslan et al. (2016, 2017). This dataset is created to provide supplementary data for our paper entitled "Autism Detection Based on Eye Movement Sequences on the Web: A Scanpath Trend Analysis Approach" presented at <a href="http://www.w4a.info/2020/">the 17th International Web for All Conference (W4A'20)</a>. This dataset includes all the individual paths used for the evaluation of our proposed approach. The dataset also includes the Python code to re-run the evaluation.</p> <p><strong>References:</strong></p> <ul> <li>Sukru Eraslan, Yeliz Yesilada, and Simon Harper. 2016. Scanpath Trend Analysis on Web Pages: Clustering Eye Tracking Scanpaths. ACM Transactions on the Web, (SCI-E), 10, 4, Article 20.</li> <li>Sukru Eraslan, Yeliz Yesilada, and Simon Harper. 2017. Engineering web-based interactive systems: trend analysis in eye tracking scanpaths with a tolerance. In Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS '17). ACM, New York, NY, USA, 3-8.</li> </ul>
Data from: Camera-based occupancy monitoring at large scales: power to detect trends in grizzly bears across the Canadian Rockies
Monitoring carnivores is critical for conservation, yet challenging because they are rare and elusive. Few methods exist for monitoring wide-ranging species over large spatial and sufficiently long temporal scales to detect trends. Remote cameras are an emerging technology for monitoring large carnivores around the world because of their low cost, non-invasive methodology, and their ability to capture pictures of species of concern that are difficult to monitor. For species without uniquely identifiable spots, stripes, or other markings, cameras collect detection/non-detection data that are well suited for monitoring trends in occupancy as its own independent useful metric of species distribution, as well as an index for abundance. As with any new monitoring method, prospective power analysis is essential to ensure meaningful trends can be detected. Here we test camera-based occupancy models as a method to monitor changes in occupancy of a threatened species, grizzly bears (Ursus arctos), at large landscape scales, across 5 Canadian national parks (~21,000 km2). With n = 183 cameras, the top occupancy model estimated regional occupancy to be 0.79 across all 5 parks. We evaluate the statistical power to detect simulated 5–40% declines in occupancy between two sampling years and test applied questions of how power is affected by the spatial scale of interest (park level vs. regional level), the number of cameras deployed, and duration of camera deployment. We also explore several ecological mechanisms (i.e., spatial patterns) of decline in occupancy, and examine how power changes when focusing only on grizzly bears family groups. As hypothesized, statistical power increased with the number of cameras and with the number of days deployed. Power was unaffected, however, by the ecological mechanisms of decline, indicating that our systematic sampling design can detect a decline regardless of whether occupancy declined due to range edge attrition, ecological trap or other mechanisms. Despite their lower occupancy, power was similarly high for grizzly bear family groups compared to grizzly bears in general. We highlight which study design attributes contributed to high power and we provide advice for establishing cost-effective camera-based programs for monitoring large carnivore occupancy at large spatial scales.
Supplementary Materials for "Challenges in the detection and attribution of Northern Hemisphere surface temperature trends since 1850"
<p><strong>Supplementary Materials for</strong>:</p> <p>R. Connolly, W. Soon, M. Connolly, S. Baliunas, J. Berglund, C.J. Butler, R.G. Cionco, A.G. Elias, V. Fedorov, H. Harde, G.W. Henry, D.V. Hoyt, O. Humlum, D.R. Legates, N. Scafetta, J.-E. Solheim, L. Szarka, V.M. Velasco Herrera, H. Yan and W.J. Zhang (<strong>2023</strong>). "Challenges in the detection and attribution of Northern Hemisphere surface temperature trends since 1850". <em>Research in Astronomy and Astrophysics</em>. <a href="https://doi.org/10.1088/1674-4527/acf18e">https://doi.org/10.1088/1674-4527/acf18e</a></p> <p><strong>Description:</strong></p> <p>A dataset for the detection and attribution of Northern Hemisphere surface temperature trends since 1850.</p> <table> <caption>Contents of dataset</caption> <thead> <tr> <th scope="col">Tab</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>NH temperatures</td> <td>5 different Northern Hemisphere (NH) annual surface temperature (ST) estimates in temperature anomalies (°C) relative to 1901-2000 averages.</td> </tr> <tr> <td>TSI 1850-2018</td> <td>27 different Total Solar Irradiance (TSI) reconstructions. At 1 Astronomical Unit (W/m2) relative to 1901-2000 average.</td> </tr> <tr> <td>IPCC AR6 forcings</td> <td>IPCC AR6 radiative forcings (RF) time series (for the 1850-2018 period) in units of W/m2</td> </tr> <tr> <td>Fitting statistics</td> <td>Fitting statistics and results from all fits</td> </tr> <tr> <td>Key results</td> <td>Summary statistics and results</td> </tr> <tr> <td>Fits - Rural-only</td> <td>Multi-linear regression fitting results for Rural-only ST</td> </tr> <tr> <td>Fits - Urban & rural</td> <td>Multi-linear regression fitting results for Urban & rural ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Fits - SST</td> </tr> </tbody> </table> </td> <td>Multi-linear regression fitting results for Sea surface temperature (SST)</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Fits - Tree-rings</td> </tr> </tbody> </table> </td> <td>Multi-linear regression fitting results for tree-ring proxy based ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Fits - Glaciers</td> </tr> </tbody> </table> </td> <td>Multi-linear regression fitting results for glacier-length proxy based ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - Rural-only</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to Rural-only ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - Urban & rural</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to urban & rural ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - SST</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to SST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - Tree-rings</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to tree-ring proxy-based ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - Glaciers</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to glacier-length proxy-based ST</td> </tr> </tbody> </table> <p> </p>
TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery
<p>TrendyGenes Literature Mining</p> <p>This repository contains the files and code to build the TrendyGenes pipeline described in the paper "TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery" (Serrano Nájera et al. 2021).</p> <p>Contents</p> <p>The folder contains the following files:</p> <ul> <li>PubMed_*.csv.gz: CSV files containing PubMed metadata (titles, abstracts etc.) split into multiple files</li> <li>CoCitations*.csv.gz: CSV files containing co-citation networks computed from PubMed</li> <li>MeSH2PMID.csv.gz: Map of MeSH terms to PMIDs</li> <li>Authorship_Neo4J_complete.csv.gz: Authorship information for PubMed papers</li> <li>Disease2PMID_Neo4J_complete.csv.gz: Map of disease terms to PMIDs after disambiguation</li> <li>Genes_Neo4J_complete_CCPU.csv.gz: Map of genes to PMIDs after disambiguation</li> <li>genes.csv.gz: List of human genes</li> <li>diseases.csv.gz: List of MeSH disease terms</li> <li>import_command*.txt: Commands to import data into Neo4j graph database</li> </ul> <p>Building the Knowledge Graph</p> <p>The various CSV files can be imported into a Neo4j graph database to build the knowledge graph containing publications, authors, genes, diseases etc. and their connections as described in the paper.</p> <p>The import_command*.txt files contain the Neo4J bulk import syntax needed to import the data into Neo4j:<br> https://neo4j.com/developer/guide-import-csv/</p> <p>Citation</p> <p>Serrano Nájera G, Narganes Carlón D, Crowther DJ. TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery. Scientific Reports. 2021 Aug 3;11(1):15747.</p> <p>License</p> <p>[MIT]</p> <p>This summarizes the key files provided and briefly explains how they can be used to build the knowledge graph database for the TrendyGenes pipeline. The citation provides a reference to the original paper.</p>
TRENDS: A Prospective Study of the Clinical Significance of Atrial Arrhythmias Detected by Implanted Device Diagnostics
ClinicalTrials.gov study NCT00279981. IPD Sharing: Not stated. Countries: 3. Publications: 2.
Data from: Camera-based occupancy monitoring at large scales: power to detect trends in grizzly bears across the Canadian Rockies
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Shifting trends: detecting changes in cetacean population dynamics in shifting habitat
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.