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123 results for “Well Tests”
Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Test Well Meteorological Station (TSWL), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.
The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Test Well Meteorological Station (TSWL). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmettswl/. These data complement and extend meteorological data recorded by an adjacent station (Met52b), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.
→ Fig. 10. FESEM images of the test structure in lagenid foraminifers from Recent, Admiralty Bay, King George Island, West Antarctica (A) and from the Jurassic of Gnaszyn, Poland (B, C). A. Unilocular Procerolagena gracilis Williamson, 1848, MWGUW ZI/67/44/02. B. Unilocular Lagena globosa Montagu, 1803, MWGUW ZI/67/61/09. C. Uniserial Nodosaria pulchra Franke, 1936, MWGUW ZI/67/61/26. Oblique cross-sectional views (A1, A2, A4, B1, B2, C); transverse cross-sectional views, showing single-crystal interlocked bundle structures, inner pores which extend along the entire length of the bundles as well as prominent calcite cleavage (A3, B3). Abbreviations: c, prominent calcite cleavage; ip, inner pore. in Chamber arrangement versus wall structure in the high-rank phylogenetic classification of Foraminifera
→ Fig. 10. FESEM images of the test structure in lagenid foraminifers from Recent, Admiralty Bay, King George Island, West Antarctica (A) and from the Jurassic of Gnaszyn, Poland (B, C). A. Unilocular Procerolagena gracilis Williamson, 1848, MWGUW ZI/67/44/02. B. Unilocular Lagena globosa Montagu, 1803, MWGUW ZI/67/61/09. C. Uniserial Nodosaria pulchra Franke, 1936, MWGUW ZI/67/61/26. Oblique cross-sectional views (A1, A2, A4, B1, B2, C); transverse cross-sectional views, showing single-crystal interlocked bundle structures, inner pores which extend along the entire length of the bundles as well as prominent calcite cleavage (A3, B3). Abbreviations: c, prominent calcite cleavage; ip, inner pore.
Data for: Testing the mettle of METAL: A comparison of phylogenomic methods using a challenging but well-resolved phylogeny
<p>Sequence alignments and gene trees for: Braun et al. "Testing the mettle of METAL: A comparison of phylogenomic methods using a challenging but well-resolved phylogeny." See README file for detailed description of all files.</p>
A Study to Test How Safe Pozelimab and Cemdisiran Combination Therapy and Cemdisiran Alone Are and How Well They Work in Adult Patients With Generalized Myasthenia Gravis
ClinicalTrials.gov study NCT05070858. IPD Sharing: YES. Countries: 19. Publications: 0.
A Study to Test if the Vaccine is Working Well in Chronic Obstructive Pulmonary Disease (COPD) Patients Aged 40 to 80 Years Old to Reduce Episodes of Worsening Symptoms and to Gather Further Informati
ClinicalTrials.gov study NCT03281876. IPD Sharing: YES. Countries: 8. Publications: 2.
This Study in Healthy Men Tests How Different Doses of BI 1358894 Are Taken up in the Body and How Well They Are Tolerated. The Study Also Looks at How Food Influences the Amount of BI 1358894 in the
ClinicalTrials.gov study NCT03210272. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
A Study to Test How Well BI 1291583 is Tolerated by People With Cystic Fibrosis Bronchiectasis (Clairafly™)
ClinicalTrials.gov study NCT05865886. IPD Sharing: YES. Countries: 6. Publications: 1.
This Study in Healthy Men Tests How Different Doses of BI 1358894 Are Taken up in the Body and How Well They Are Tolerated. The Study Also Tests How BI 1358894 Affects the Way the Body Breaks Down Mid
ClinicalTrials.gov study NCT03754959. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
This Study Tests Whether BI 409306 Prevents Patients With a Specific Type of Mental Illness (Attenuated Psychosis Syndrome) From Becoming Worse. This Study Looks at How Well Patients Tolerate the Medi
ClinicalTrials.gov study NCT03230097. IPD Sharing: YES. Countries: 4. Publications: 2.
This Study Tests Whether BI 409306 Prevents Patients With Schizophrenia From Becoming Worse. This Study Looks at How Well Patients Tolerate BI 409306 and How Effective it is Over 6 Months
ClinicalTrials.gov study NCT03351244. IPD Sharing: YES. Countries: 7. Publications: 1.
A Study to Test How Well Empagliflozin Works in Japanese People With Type 2 Diabetes Who Are Older Than 65 Years
ClinicalTrials.gov study NCT04531462. IPD Sharing: YES. Countries: 1. Publications: 1.
This Study is Done in Patients With Plaque Psoriasis and Tests How Well They Tolerate BI 730357 and How Effective it is
ClinicalTrials.gov study NCT03635099. IPD Sharing: YES. Countries: 3. Publications: 2.
Testing a Wellness App for First Responders, Military Personnel and Veterans
ClinicalTrials.gov study NCT06336967. IPD Sharing: NO. Countries: 1. Publications: 1.
A Study to Test How Well Patients With Plaque Psoriasis Tolerate BI 730357 Over a Longer Period and How Effective it is
ClinicalTrials.gov study NCT03835481. IPD Sharing: YES. Countries: 3. Publications: 2.
A Machine Learning-Based Method for Classifying Well Test Responses in Naturally Fractured Reservoirs
<p>Complete dataset (raw data), processing codes (MATLAB v2020b) and numerical simulation model (Petrel v2017, ECLIPSE) for clustering of pressure derivatives in Naturally Fractured Reservoirs</p> <p> </p> <p> </p>
Fig. 5 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 5. Visualization of a feature with high importance and a feature with low importance from a configuration B. The importance of these features for the identification accuracy was determined using permutation tests (see the methods). For each taxon or group (rows) several randomly selected specimens (columns) are shown. For the two selected Global Average Pooling layer features, the corresponding features of the preceding (Max Pooling) layer are visualized as those show specific image parts that had higher activations.Yellow represents the maximal activation strength; dark blue represents the minimal activation strength.
Fig. 2 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 2. Schematic representation of the networks used. On top are the convolutional layers of VGG16, grouped into five blocks. Output of each Max Pooling layer is fed into Global Average Pooling layer. Numbers near each block name indicate number of features in the Global Average Pooling layer. Height of layers roughly corresponds to resolution (except for Global Average Pooling layer), while width roughly corresponds to the number of feature maps or features produced.Then, in approach A, outputs of five blocks are concatenated and passed to the linear classifier. In approach B, output of only one block (block 3 in the final configuration) is passed to the linear classifier. In approach C CNN outputs are as in approach A, but instead connected to a DNN with two layers of 320 fully connected (FC) neurons followed by a prediction layer (PL), with number of neurons equal to number of species classified. Finally, approach D features CNN as in approach B which is connected to DNN as in approach C.
Fig. 1 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 1. Dorsal habitus photos of males and females of Tuxedo spp., Pygovepres vaccinicola, and Phallospinophylus setosus generated for and used in this study.
Fig. 4 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 4. Validation accuracy and accuracy on test data for SVM linear classifier (A, B) and DNN approaches (C, D) for the three datasets.
Fig. 3 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 3. Identification accuracy for the male (top) and female (bottom) Tuxedo dataset for the five selected resolutions and the individual blocks 1–5 and the concatenated block.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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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.