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19 results for “observer bias”
Sparse observations induce large biases in estimates of the global ocean CO2 sink: an ocean model subsampling experiment
<p>Dataset underlying the analysis in Hauck et al., 2023: Sparse observations induce large biases in estimates of the global ocean CO<sub>2</sub> sink - an ocean model subsampling experiment, Philosophical Transactions A</p> <p>Surface ocean partial pressure of CO<sub>2 </sub>(pCO<sub>2</sub>) and air-sea CO<sub>2</sub> flux reconstructions, using two mapping methods (MPI-SOM-FFN, CarboScope) three different sampling masks: SOCAT, SOCAT+SOCCOM, IDEAL (based on bgcArgo, Roemmich et al., 2019).</p> <p>Also, all FESOM-REcoM output fields that were used in the reconstructions are provided.</p> <p>We further provide the three masks that were used for subsampling: SOCAT, SOCAT+SOCCOM, IDEAL (bgcArgo).</p> <p> </p>
Observational Bias and Young Massive Cluster Characterisation II. Can Gaia accurately observe young clusters and associations?
<p>Field-of-View for synthetic Gaia observations of clusters Orion-type-3, Orion-type-5.5 and Wd2-type presented in Buckner et al. (2023).</p><p>Files contain both simulation and field stars along the Line-of-Sight (l = 270^o, b = 0^o) for the clusters when placed at 500pc, 2500pc and 4300pc distances.</p><p>The original simulation files are included for reference.</p><p>Included README files provide more detailed descriptions of the files.</p>
Artificial night light helps account for observer bias in citizen science monitoring of an expanding large mammal population
1. The integration of citizen scientists into ecological research is transforming how, where, and when data are collected, and expanding the potential scales of ecological studies. Citizen-science projects can provide numerous benefits for participants, while educating and connecting professionals with lay audiences, potentially increasing acceptance of conservation and management actions. However, for all the benefits, collection of citizen-science data is often biased towards areas that are easily accessible (e.g. developments and roadways), and thus data are usually affected by issues typical of opportunistic surveys (e.g. uneven sampling effort). These areas are usually illuminated by artificial light at night (ALAN), a dynamic sensory stimulus that alters the perceptual world for both humans and wildlife. 2. Our goal was to test whether satellite-based measures of ALAN could improve our understanding of the detection process of citizen scientist-reported sightings of a large mammal. 3. We collected observations of American black bears (Ursus americanus; n = 1,315) outside their primary range in Minnesota, USA, as part of a study to gauge population expansion. Participants from the public provided sighting locations of bears on a website. We used an occupancy modelling framework to determine how well ALAN accounted for observer metrics when compared to other commonly used metrics (e.g. housing density). 4. Citizen scientists reported 17% of bear sightings were under artificially-lit conditions and monthly ALAN estimates did the best job accounting for spatial bias in detection of all observations, based on AIC values and effect sizes (β ^ = 0.81, 0.71 – 0.90 95% CI). Bear detection increased with elevated illuminance; relative abundance was positively associated with natural cover, closer proximity to primary bear range and lower road density. Although the highest counts of bear sightings occurred in the highly illuminated suburbs of the Minneapolis-St. Paul metropolitan region, we estimated substantially higher bear abundance in another region with plentiful natural cover and low ALAN (up to 275% increased predicted relative abundance) where observations were sparse. 5. We demonstrate the importance of considering ALAN radiance when analyzing citizen scientist-collected data, and we highlight the ways that ALAN data provides a dynamic snapshot of human activity. 31-Jul-2020
Geolocators lead to better measures of timing and renesting in Black-tailed Godwits and reveal the bias of traditional observational methods
<p>Long‐term population studies can identify changes in population dynamics over time. However, to realize meaningful conclusions, these studies rely on accurate measurements of individual traits and population characteristics. Here, we evaluate the accuracy of the observational methods used to measure reproductive traits in individually marked black‐tailed godwits (<i>Limosa limosa limosa</i>). By comparing estimates from traditional methods with data obtained from light‐level geolocators, we provide an accurate estimate of the likelihood of renesting in godwits and the repeatability of the lay dates of first clutches. From 2012 – 2018, we used periods of shading recorded on the light‐level geolocators carried by 68 individual godwits to document their nesting behaviour. We then compared these estimates to those simultaneously obtained by our long‐term observational study. We found that among recaptured geolocator‐carrying godwits, all birds renested after a failed first clutch, regardless of the date of nest loss or the number of days already spent incubating. We also found that 43% of these godwits laid a second replacement clutch after a failed first replacement, and that 21% of these godwits renested after a hatched first clutch. However, the observational study correctly identified only 3% of the replacement clutches produced by geolocator‐carrying individuals and designated as first clutches a number of nests that were actually replacement clutches. Additionally, on the basis of the observational study, the repeatability of lay date was 0.24 (95% CI 0.17 – 0.31), whereas it was 0.54 (95% CI 0.28 – 0.75) using geolocator‐carrying individuals. We use examples from our own and other godwit studies to illustrate how the biases in our observational study discovered here may have affected the outcome of demographic estimates, individual‐level comparisons, and the design, implementation, and evaluation of conservation practices. These examples emphasize the importance of improving and validating field methodologies and show how the addition of new tools can be transformational.</p>
Archived Model Output and Code for "Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations "
<div> <p>This is an archive of CAM6 simulation output used in the paper Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations, submitted to the AGU Journal. Codes used to read the nc file is also attached.</p> </div>
Replication of manuscript entitled "Multi-decadal climate variability and satellite biases have amplified model-observation discrepancies in tropical troposphere warming estimates"
<p>Replication of manuscript entitled "Multi-decadal climate variability and satellite biases have amplified model-observation discrepancies in tropical troposphere warming estimates".</p> <p>This page contains datasets used to replicate figures in a manuscript entitled "Multi-decadal climate variability and satellite biases have amplified model-observation discrepancies in tropical troposphere warming estimates". These ascii or netcdf files can be easily read by NCL, Fortran and others.</p> <p> </p>
Risk of bias in observational studies using routinely collected data of comparative effectiveness research: a meta-research study
<p>We performed a meta-research study by searching PubMed for comparative effectiveness observational studies evaluating therapeutic interventions using routinely collected data published in high impact factor journals from 01/06/2018 to 30/06/2020. We assessed the reporting of study design (i.e., eligibility, treatment assignment, and the start of follow-up). Risk of selection bias and immortal time bias was determined by assessing if the time of eligibility, treatment assignment and the start of follow-up were synchronised to mimic the randomisation following the target trial emulation framework.</p>
Artificial night light helps account for observer bias in citizen science monitoring of an expanding large mammal population
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Geolocators lead to better measures of timing and renesting in Black-tailed Godwits and reveal the bias of traditional observational methods
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Calibration Bias Evaluation and Correction of S-Band Ground-Based Radar Reflectivity Using Ku-Band Space-Borne Radar Observations Along the East Coast of India
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Observation of plateau regions for zero bias peaks within 5% of the quantized conductance value 2e2/h
<p>This repository contains the raw data and processing Python scripts corresponding to the paper "Observation of plateau regions for zero bias peaks within 5% of the quantized conductance value 2e2/h"</p>
The observed mass distribution of Galactic black hole LMXBs is biased against massive black holes
<p>Jupyter Notebook plus necessary data files to recreate the figures, the numbers reported, and a table in the publication.</p> <p>Also available at: <a href="http://arxiv.org/abs/2104.03596">http://arxiv.org/abs/2104.03596</a></p> <p> </p>
Data from: Prolific observer bias in the life sciences: why we need blind data recording
Observer bias and other "experimenter effects" occur when researchers' expectations influence study outcome. These biases are strongest when researchers expect a particular result, are measuring subjective variables, and have an incentive to produce data that confirm predictions. To minimize bias, it is good practice to work "blind," meaning that experimenters are unaware of the identity or treatment group of their subjects while conducting research. Here, using text mining and a literature review, we find evidence that blind protocols are uncommon in the life sciences and that nonblind studies tend to report higher effect sizes and more significant p-values. We discuss methods to minimize bias and urge researchers, editors, and peer reviewers to keep blind protocols in mind.
Observation Bias in Metabarcoding
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Data and code for "Logistic and preference bias in participatory science butterfly observations"
<p>This repository contains all code needed to replicate the analyses performed in "Identification ease, wing pattern diversity, and family explain taxonomic bias in butterfly observations" by Goldstein, Stoudt, Lewthwait, Shirey, Mendoza, and Guzman.</p> <p> </p> <p>Components</p> <p>The main code to be executed is contained in the "code" folder and should be run in numeric order (00 through 03) with two helper scripts.</p> <p>All but one of the data inputs can be found in the "data" folder. The iNaturalist data is required but not published within this directory. It may be obtained from GBIF at <a href="https://doi.org/10.15468/dl.rhmxtn">this link</a> and should be saved into the "data" folder.</p> <p>Note that, at the request of eButterfly, we have removed location information for eButterfly observations of sensitive species in this repository. Please contact eButterfly directly for the uncensored data.</p> <p> </p> <p>Other notes</p> <p>The code in the "01" file takes a long time to run (all of the GAMs for each species). You can skip ahead to "02" to run meta-analysis code on the species-level results, which we provide in "output" as "estimated_indices_fromGAMs.csv".</p>
Data from: Prolific observer bias in the life sciences: why we need blind data recording
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Decline in IGF1 in CXCL12-Abundant Reticular (CAR) Cells Causes Myeloid-Biased Hematopoiesis Observed During Aging
GEO Series GSE210584. Mus musculus. 14 samples. Type: Expression profiling by high throughput sequencing.
Dissociation Protocols used for Sarcoma Tissues Bias the Transcriptome observed in Single-cell and Single-nucleus RNA sequencing.
GEO Series GSE200529. Homo sapiens. 26 samples. Type: Expression profiling by high throughput sequencing.
Data from: Revealing biases in insect observations a comparative analysis between academic and citizen science data
<p>Data and code used in the article "Revealing biases in insect observations a comparative analysis between academic and Citizen Science data".</p>
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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.