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93 results for “TSE”
Florida Coastal Everglades site, station Taylor Slough Trexler Site TSE, study of animal density of Heterandria formosa in units of numberPerMeterSquared on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Florida Coastal Everglades (FCE) contains animal density of Heterandria formosa measurements in numberPerMeterSquared units and were aggregated to a yearly timescale.
Florida Coastal Everglades site, station Taylor Slough Trexler Site TSE, study of animal density of Enneacanthus gloriosus in units of numberPerMeterSquared on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Florida Coastal Everglades (FCE) contains animal density of Enneacanthus gloriosus measurements in numberPerMeterSquared units and were aggregated to a yearly timescale.
Florida Coastal Everglades site, station Taylor Slough Trexler Site TSE, study of animal density of Lepomis punctatus in units of numberPerMeterSquared on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Florida Coastal Everglades (FCE) contains animal density of Lepomis punctatus measurements in numberPerMeterSquared units and were aggregated to a yearly timescale.
Florida Coastal Everglades site, station Taylor Slough Trexler Site TSE, study of animal density of Elassoma evergladei in units of numberPerMeterSquared on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Florida Coastal Everglades (FCE) contains animal density of Elassoma evergladei measurements in numberPerMeterSquared units and were aggregated to a yearly timescale.
Florida Coastal Everglades site, station Taylor Slough Trexler Site TSE, study of animal density of Cichlasoma urophthalma in units of numberPerMeterSquared on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Florida Coastal Everglades (FCE) contains animal density of Cichlasoma urophthalma measurements in numberPerMeterSquared units and were aggregated to a yearly timescale.
Florida Coastal Everglades site, station Taylor Slough Trexler Site TSE, study of animal species richness of Osteichthyes in units of number on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Florida Coastal Everglades (FCE) contains animal species richness of Osteichthyes measurements in number units and were aggregated to a yearly timescale.
Complementary materials for: Looking For Novelty in Search-based Software Product Line Testing (TSE)
<p>In this repository, we provide complementary materials for the following paper: </p> <pre>Y. Xiang, H. Huang, M. Li, S. Li and X. Yang, "Looking For Novelty in Search-based Software Product Line Testing" in <em>IEEE Transactions on Software Engineering</em>, vol. , no. 01, pp. 1-1, 5555. doi: 10.1109/TSE.2021.3057853 url: https://doi.ieeecomputersociety.org/10.1109/TSE.2021.3057853</pre> <p>1. Correlation analysis results (Pearson'r and p-value) are tabulated in <a href="https://www.zenodo.org/api/files/f6451296-91dc-4c9c-89fc-28ebf315be10/CorrelationAnalysisResults.xlsx?versionId=2f4ec2ab-3e48-420d-9819-b5e60ea43c06">CorrelationAnalysisResults.xlsx</a>; </p> <p>2. MATLAB scripts and raw data, which were used to perform correlation analyses, are given in <a href="https://zenodo.org/api/files/f6451296-91dc-4c9c-89fc-28ebf315be10/RawDataForCorrelationAnalysis.rar?versionId=5a168e2e-df32-4773-b8f3-4bf49de8d01d">RawDataForCorrelationAnalysis.rar</a>. </p> <p>---------------------------------------------------------------------------------------------------------------------------------------</p> <p>We suggest reproducing the correlation analysis, and other experiments described in the paper, using the codes provided at Github: <a href="https://github.com/gzhuxiangyi/TSE_NS">https://github.com/gzhuxiangyi/TSE_NS </a></p> <p> </p>
The Artifact of the TSE Submission #TSE-2022-12-0520
<p>In this online repository, we open source the source code of each standalone technique as well as the fusion approaches implemented by ourselves (stored in the <em>Fusion_approach.zip</em> file).</p> <p> </p> <p>Our evaluation is based on the large-scale CodeSearchNet dataset, which can be accessed through its <a href="https://github.com/github/CodeSearchNet">official webpage</a>.</p> <p> </p> <p>We also provide the experimental results we obtained in our study, including the rank and score results from each standalone techniques (stored in the <em>Standalone_Java.zip</em> and <em>Standalone_Python.zip</em> files) and the fusion results (stored in the <em>Fusion_results.zip</em> file).</p>
Artifacts of the paper under review by TSE
<p>This is the online repository of <strong>*Predictive Comment Updating with Heuristics and AST-Path-Based Neural Learning: A Two-Phase Approach</strong>*, a research paper under review by TSE. We release the source code and relevant data of <strong>Toper</strong>, the data used in our evaluation, as well as the experiment results.</p> <ul> <li><strong>Dataset</strong></li> </ul> <p>Basically, the dataset is from Liu et al.'s ASE20 paper (i.e., <a href="conf.researchr.org/details/ase-2020/ase-2020-papers/3/Evaluating-Representation-Learning-of-Code-Changes-for-Predicting-Patch-Correctness-i">Automating Just-In-Time Comment Updating</a>), and then cleaned by Lin et al.'s ICPC21 paper (i.e., <a href="https://conf.researchr.org/details/icpc-2021/icpc-2021-research/20/Automated-Comment-Update-How-Far-are-We-">Automated Comment Update: How Far are We?</a>). We classify the dataset into code-indicative and non-code-indicative items and store them in <code>Data</code> directory, which is named by the format of <em>[data catagory]_Items_[Dataset].jsonl</em>. For example, <strong>All_Item_Test.jsonl</strong> means this file includes all (i.e., including code-indicative and non-code-indicative) items in the test set. Similarly, <strong>NCIU_Items_Test.json</strong> means this file only covers non-code-indicative items in the test set.</p> <ul> <li><strong>The Code-Indicative Update Classifier</strong></li> </ul> <p>We design a classifier to differentiate the Code-Indicative and Non-Code-Indicative updates. The replication package is available at <strong>Code/TypeClassifier.py</strong>. To obtain the result of the classifier, please run the following command:</p> <pre><code class="language-python">python3 TypeClassifier.py -training/FilePath FeaturesForClassifier/featuresForTrain.csv -testFilePath FeaturesForClassifier/featuresForTest.csv</code></pre> <ul> <li><strong>Operation Path Extractor</strong></li> </ul> <p>The customized tool for extracting operation path from the dataset is provided by previous studies. To obtain the preprocessed data, run the following command:</p> <pre><code class="language-java">java -cp OperationPathExtractor.jar Extractor.App --data_dir path/to/data --input_name semi-finished/data/path --output_name path/to/store/data --num_threads 1 The preprocessed data are stored in Data/Preprocessed.</code></pre> <ul> <li><strong>The Non-Code-Indicative Comment Updater</strong></li> </ul> <p>Our replication code is available at <code>Code</code>, and the detail instructions of command are at <code>comment_update.py</code>. Or you can simply execute the following command:</p> <pre><code class="language-python">python3 comment_update.py -data_path path/to/data -gpu -use_features</code></pre> <p> </p>
Cocreating free games for kids with asthma: interview with Sze Man Tse, respirologist
<p>Dr Sze Man Tse from Sainte-Justine university hospital, shares her experience of the game jam held in August 2016 in Montreal.</p> <p>About the initiative and next game jams: www.breathinggames.net</p> <p>Interview: Fabio Balli<br> Music: John Danger<br> Post-production: Amélie Bouita, Fabio Balli, Valentin Gomez</p> <p>Supported by Sainte-Justine university hospital, Concordia University, The Lung Association Quebec, Sensorica.</p>
Videos for deeplabcut, noldus ethovision X14 and TSE multi conditioning systems comparisons
<p>Supplementary top view videos of OFT (open field test), EPM (elevate plus maze) and FST (foreced swim test) of adult male mice for deeplabcut, noldus ethovision X14 and TSE multi conditioning systems comparisons. All code used for analysis of these videos can be found under: https://github.com/ETHZ-INS/DLCAnalyzer. Additionally, DeepLabCut output files (point tracking data) of the videos and manual annotation of behaviors can be found in the same github repository. Additionally, the publication associated with this data here can be found under https://www.biorxiv.org/content/10.1101/2020.01.21.913624v1</p>
mDixon TSE MRI Sequence And Conventional MRI Sequences In Dysimmune Orbitopathies (DDX)
ClinicalTrials.gov study NCT02397109. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study Evaluating TSE-424/Placebo/Raloxifene in Preventing Osteoporosis in Postmenopausal Women
ClinicalTrials.gov study NCT00481169. IPD Sharing: Not stated. Countries: 0. Publications: 0.
ScienceDex guides
Understand access before you commit
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.