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3,145 results for “Well Being”
IODP Expedition 340, Hole U1399C - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
IODP Expedition 340, Hole U1397B - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
IODP Expedition 349, Hole U1431E - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
IODP Expedition 351, Hole U1438D - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
IODP Expedition 352, Hole U1442A - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
IODP Expedition 353, Hole U1445A - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
IODP Expedition 354, Hole U1450B - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
IODP Expedition 354, Hole U1453A - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
IODP Expedition 352, Hole U1439C - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
IODP Expedition 355, Hole U1456C - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
IODP Expedition 351, Hole U1438F - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
IODP Expedition 351, Hole U1438E - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
Assessing human well-being constructs with environmental and equity aspects: A review of the landscape
<p>Coded data for Betley et al. 2021. Assessing human well-being constructs with environmental and equity aspects: A review of the landscape. People and Nature. </p>
WYC seismic phases for "Lighting up an 1-km fault near a hydraulic fracturing well using machine-learning based picker"
<p>In this study, we applied a state-of-the-art package on newly collected nodal-array data around a hydraulic-fracturing well. The array consists of up to 85 nodes with an average station spacing of less than a kilometer. Within the hydraulic-fracturing stimulation weeks, we detected ~3000 seismic events with magnitude down to ~-2. </p> <p>The seismic phases to associate the events are included in Zenodo_share.zip. The final catalog and station locations (in relative scale) are included in the excel spreadsheet.</p>
A Comprehensive Physical Model for the Contrasting Seismogenic Behaviour of Injection Wells in Western Canada
<p>Earthquake catalog for northern Montey play in northeastern British Columbia during 2017-2018.</p>
A complete list of filtered variants and gene lists of frequently and recurrently mutated genes in DLBCL, MCL, T-NHL, and BL, as well as pre-assembled CNV gene list
<p>A complete list of variants which passed filtering described in supplemental methods, that were found in both PDX model sample and patient’s sample from which it was derived (S1A) and variants which were gained (S1B) or lost (S1C) during PDX model derivation. Gene lists for filtration of variants in genes of special interest are included in the table (S1D). Gene list for filtration of CNV changes in genes of special interest is included in the table (S1E). Chr - Chromosome, REF - Reference allele, ALT - Alternative allele, AA change - Amino acid change, Patient AF - Allele frequency in the patient’s sample, Patient Depth – Read depth in patient’s sample, PDX AF- Allele frequency in PDX model sample, PDX Depth - Read depth in PDX sample, DLBCL - Diffuse Large B-cell lymphoma, MCL - Mantle Cell Lymphoma, TCL - T-cell lymphoma, BL – Burkitt Lymphoma, and CNV - Copy Number Variation.</p>
The mean annual groundwater-level trends observed by 452 monitoring wells in the North China Plain during 2003-2016
<p>The mean annual groundwater-level (GWL) trends observed by 452 monitoring wells (341 unconfined and 111 confined) in the North China Plain (NCP) during 2003-2016. The monthly GWL data were obtained from the Chinese groundwater management authority and the groundwater yearbooks. The wells were relatively evenly distributed in NCP region and each well follows a quality control with at least 60% of temporal coverage of the study period having GWL data records.</p>
Mutualism has its limits: consequences of asymmetric interactions between a well-defended plant and its herbivorous pollinator
<p>Concern for pollinator health has focused on social bees and their agricultural services, but not all pollinators are bees, and their ecosystem services also promote biodiversity and conservation. Pollinating herbivores generate ecological conflicts when they utilize the same plant as a nectar source and larval host. We tracked individual-level metrics of pollinator health – growth, survivorship, fecundity – across the life cycle of a pollinating herbivore, the hawkmoth Hyles lineata, through its interactions with Oenothera harringtonii, a rare plant polymorphic for the floral volatile (R)-(-)-linalool. Linalool had no impact on moth attraction to O. harringtonii flowers but suppressed oviposition on experimentally supplemented plants. Leaves of O. harringtonii showed robust resistance against herbivory by H. lineata from leaf-disc to wholeplant scales, through poor larval growth and survivorship. Higher larval performance on other Oenothera species indicates that constitutive herbivore resistance by O. harringtonii is not generic. Leaf volatiles differed among populations of O. harringtonii but were not induced by larval herbivory. Elagitannins and other phenolics varied among leaf, bud and seed tissues but showed no evidence of herbivore induction. Our findings highlight asymmetric plant-pollinator interactions and the importance of third parties, such as alternative host plants, in maintaining pollinator health.</p>
Data from: How well do embryo development rate models derived from laboratory data predict embryo development in sea turtle nests?
<p>Development rate of ectothermic animals varies with temperature. Here we use data derived from laboratory constant temperature incubation experiments to formulate development rate models that can be used to model embryonic development rate in sea turtle nests. We then use a novel method for detecting the time of hatching to measure the in situ incubation period of sea turtle clutches to test the accuracy of our models in predicting the incubation period from nest temperature traces. We found that all our models overestimated the incubation period. We hypothesize three possible explanations which are not mutually exclusive for the mismatch between our modeling and empirically measured in situ incubation period: (1) a difference in the way the incubation period is calculated in laboratory data and in our field nests, (2) inaccuracies in the assumptions made by our models at high incubation temperatures where there is no empirical laboratory data, and (3) a tendency for development rate in laboratory experiments to be progressively slower as temperature decreases compared with in situ incubation.</p>
Publication release: How well do species distribution models predict occurrences in exotic ranges?
<div class="record-description"> <p>Species distribution models (SDMs) are widely used predictive tools to forecast potential biological invasions. However, the reliability of SDMs extrapolated to exotic ranges remains understudied, with most analyses restricted to few species and equivocal results. We examined the spatial transferability of SDMs for 647 non-indigenous species extrapolated across 1,867 invaded ranges, and identify what factors may help differentiate predictive success from failure. We performed a large-scale assessment of the transferability of SDMs using two modelling approaches: generalized additive models (GAMs) and MaxEnt. We fitted SDMs on the native ranges of species and extrapolated them to exotic ranges. We examined the influence of general factors and factors related to biological invasions on spatial transferability.</p> <p>Here, we provide the code and data for publication in Global Ecology and Biogeography as part of Nguyen and Leung 2022 "How well do species distribution models predict occurrences in exotic ranges?". Provided are the files and scripts necessary to fit and validate the SDMs using distirbutional data from their native and exotic ranges, respectively, formulated as generalized additive models (GAMs) or MaxEnt models. Additionally, provided is a script to validate the SDMs on their native fitting range using 10-fold cross-validation, and to fit the transferability model, as a linear mixed model (LMM), with a provided cleaned data.frame. The dataset provided includes a full species list with GBIF occurrence records, target-group background (TGB) records to use with model fitting and validation, as well as environmental data associated with the sightings.</p> </div>
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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)
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.