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10 results for “Debiasing”
Additional Material for Debiasing architectural decision-making: a workshop-based training approach
<p>This additional material was created to enable full replication of the experiment described in the paper:<br> "Klara Borowa, Maria Jarek, Gabriela Mystkowska, Weronika Paszko and Andrzej Zalewski: <strong>Debiasing architectural decision-making: a workshop-based training approach</strong>".<br> The paper was accepted for publication at ECSA 2022 (The 16th European Conference on Software Architecture).<br> <br> The folder contains the following files:<br> Coding-details.xlsx - the coding scheme, as well as coding results. Contains all code counts for each team participating in the experiment.<br> Experiment-meeting-plan.pdf - contains the plan for the 3-hour meeting during which the experiment was conducted, as well as a timetable of when each meeting took place and who organised it.<br> Debiasing-workshop-slides.pdf - slides used during the debiasing workshop.<br> Debiasing-workshop-plan.pdf - detailed instruction for workshop organisers, what should be said and done during the workshop.<br> Participants_questionnaire_and_results.xlsx - questionnaire questions used to obtain information about participants, as well as questionnaire results for all 44 participants.</p>
Additional Material for Debiasing architectural decision-making: a workshop-based training approach
<p>This additional material was created to enable full replication of the experiment described in the paper:<br> "Klara Borowa, Maria Jarek, Gabriela Mystkowska, Weronika Paszko and Andrzej Zalewski: <strong>Debiasing architectural decision-making: a workshop-based training approach</strong>".<br> The paper was accepted for publication at ECSA 2022 (The 16th European Conference on Software Architecture).<br> <br> The folder contains the following files:<br> Coding-details.xlsx - the coding scheme, as well as coding results. Contains all code counts for each team participating in the experiment.<br> Experiment-meeting-plan.pdf - contains the plan for the 3-hour meeting during which the experiment was conducted, as well as a timetable of when each meeting took place and who organised it.<br> Debiasing-workshop-slides.pdf - slides used during the debiasing workshop.<br> Debiasing-workshop-plan.pdf - detailed instruction for workshop organisers, what should be said and done during the workshop.<br> Participants_questionnaire_and_results.xlsx - questionnaire questions used to obtain information about participants, as well as questionnaire results for all 44 participants.</p>
The SLACS strong lens sample, debiased
<p>Datasets from the article "The SLACS strong lens sample, debiased" (Sonnenfeld 2024, A&A in press).</p> <p> </p> <p><strong>Content</strong></p> <p><code>SLACS_table.cat</code>: table with data of the SLACS lenses. <code>parent_sample.fits</code>: .fits file with data of the parent sample. <code>full_inference.hdf5</code>: chain of samples from the posterior probability distribution of the full model. <code>nofpprior_inference.hdf5</code>: chain of samples from the posterior probability distribution of the model with no fundamental plane prior. <code>slonly_inference.hdf5</code>: chain of samples from the posterior probability distribution of the lens-only model, with <strong>no</strong> selection function correction. <code>full_pp_samples.hdf5</code>: posterior predicted samples from the full model. <code>full_nopfind_pp_samples.hdf5</code>: posterior predicted samples from the full model, with no lens finding probability term. This is used to predict the population of detectable lenses.</p> <div> <h3>Chain files</h3> </div> <p>File <code>full_inference.hdf5</code> contains the chain with samples from the posterior probability. Samples in each of the parameters are stored in separate datasets. The shape of each dataset is (100, 2000), with the first dimension being the number of walkers in the MCMC. The datasets are the following:</p> <table> <tbody> <tr> <th>Name</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>mu_m5</td> <td>Mean m5 at log-stellar mass 11.3 and average size</td> </tr> <tr> <td>mu_gamma</td> <td>Mean gamma at log-stellar mass 11.3 and average size</td> </tr> <tr> <td>beta_m5</td> <td>Dependence of m5 on stellar mass</td> </tr> <tr> <td>xi_m5</td> <td>Dependence of m5 on excess size</td> </tr> <tr> <td>beta_gamma</td> <td>Dependence of gamma on stellar mass</td> </tr> <tr> <td>xi_gamma</td> <td>Dependence of gamma on excess size</td> </tr> <tr> <td>sigma_m5</td> <td>Intrinsic scatter in m5 around the mean</td> </tr> <tr> <td>sigma_gamma</td> <td>Intrinsic scatter in gamma around the mean</td> </tr> <tr> <td>t_find</td> <td>Parameter <code>theta_0</code> of the lens finding probability</td> </tr> <tr> <td>la_find</td> <td>Log-10 of parameter <code>a</code> of the lens finding probability</td> </tr> <tr> <td>fpfit</td> <td>Parameters of the fundamental plane fit</td> </tr> <tr> <td>logp</td> <td>log of posterior probability</td> </tr> </tbody> </table> <p>The file <code>slonly_inference.hdf5</code> is similarly structured.</p> <div> <h3>Posterior predicted samples</h3> </div> <p>The file <code>full_pp.hdf5</code> contains posterior predicted samples of properties of parent sample galaxies and strong lenses. It contains 1000 draws from the posterior. The data are organised in groups, as follows:</p> <ul> <li><code>hyperpars</code>: values of the model parameters.</li> <li><code>subset</code>: properties of a random sample of 100 galaxies (different seed for each sample).</li> <li><code>lenses</code>: properties of a sample of 59 lenses.</li> <li><code>pop_sigma_bin</code>: average properties of parent sample galaxies, in bins of velocity dispersion. Bin edges are defined in <code>sigma_bins</code>.</li> <li><code>pop_ms_bin</code>: average properties of parent sample galaxies, in bins of log-stellar mass. Bin edges are defined in <code>ms_bins</code>.</li> <li><code>lens_sigma_bin</code>: average properties of parent sample galaxies, in bins of velocity dispersion.</li> <li><code>lens_ms_bin</code>: average properties of parent sample galaxies, in bins of log-stellar mass.</li> </ul> <p>The file <code>full_nopfind_pp.hdf5</code> is similarly structured.</p>
Additional Material for: Debiasing Architectural Decision-Making: An Experiment With Students and Practitioners
<p>Additional Material for: Debiasing Architectural Decision-Making: An Experiment With Students and Practitioners submitted to ICSA 2025.<br><br>The files contain:<br>1. README.md - A short file describing the contents of the replication package.<br>2. Coding guide.docx - a guide used for coding, with examples from the experiment's participants.<br>3. Coding_results.xlsx - data with all measurements for all participants.<br>4. Questionnaire.xlsx - questionnaire used to gather demographic participant's data.<br>5. Experiment-plan.docx - the detailed plan of all the experiment steps.<br>6. Debiasing-workshop-slides.pptx - slides used during the debiasing workshop.<br>7. Debiasing-workshop-plan.docx - a detailed plan of the debiasing workshop, explaining how exactly the debiasing presentation should be used.<br>8. Consent_form.docx - a consent form that was signed by the participants from one of the author's countries, where guidelines suggested the use of gathering additional written consent.<br><br></p>
Data for the manuscript 'Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework'.
<p>This zip file contains the data downloaded from external sources used to produce the results in the manuscript 'Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework'. Note that all of the data contained in this zip file was publicly available at the time of writing.</p> <p>The corresponding Github can be found here:<br> https://github.com/alan-turing-institute/jbc-turing-rss-testdebiasing</p> <p>The publication is available here:<br> https://doi.org/10.1038/s41564-021-01029-0</p>
Data from: Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD
Increasingly, ecological modellers are integrating paleodata with future projections to understand climate-driven biodiversity dynamics from the past through the current century. Climate simulations from earth system models are necessary to this effort, but must be debiased and downscaled before they can be used by ecological models. Downscaling methods and observational baselines vary among researchers, which produces confounding biases among downscaled climate simulations. We present unified datasets of debiased and downscaled climate simulations for North America from 21 ka BP to 2100AD, at 0.5° spatial resolution. Temporal resolution is decadal averages of monthly data until 1950AD, average climates for 1950–2005 AD, and monthly data from 2010 to 2100AD, with decadal averages also provided. This downscaling includes two transient paleoclimatic simulations and 12 climate models for the IPCC AR5 (CMIP5) historical (1850–2005), RCP4.5, and RCP8.5 21st-century scenarios. Climate variables include primary variables and derived bioclimatic variables. These datasets provide a common set of climate simulations suitable for seamlessly modelling the effects of past and future climate change on species distributions and diversity.
Additional Material for Debiasing Architectural Decision-Making: Teaching Software Practitioners
<p>Additional material for the paper subsmission "Debiasing Architectural Decision-Making: Teaching Software Practitioners".<br><br>Coding_info.xlsx - Code description as well as code measurements for all coded values.<br>Experiment-plan.docx - Instructions used by authors which performed the experiment.<br>Debiasing-workshop-plan.docx - Instruction for the teacher conducting the workshop, specifying what should be done while showcasing particular presentation slides.<br>Debiasing-workshop-slides.pptx - Slides used during the debiasing workshop.<br>Questionaire_and_test.xlsx - Questionnaire used to gather information about the participants + Simple test used as the last step of the workshop.</p>
Data from: Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD
Open the record for dataset details and reuse information.
Additional Material for Debiasing Architectural Decision-making: An Experiment With Software Practitioners
Open the record for dataset details and reuse information.
Testing an AI Large Language Model Tool for Cognitive Debiasing in Musculoskeletal Care
ClinicalTrials.gov study NCT07022769. IPD Sharing: NO. Countries: 1. Publications: 0.
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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
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.