Selection bias is an important but often neglected problem in comparative research. While comparative case studies pay some attention to this problem, this is less the case in broader cross-national studies, where this problem may appear through the way the data used are generated. The article discusses three examples: studies of the success of newly formed political parties, research on protest events, and recent work on ethnic conflict. In all cases the data at hand are likely to be afflicted by selection bias. Failing to take into consideration this problem leads to serious biases in the estimation of simple relationships. Empirical examples illustrate a possible solution (a variation of a Tobit model) to the problems in these cases. The article also discusses results of Monte Carlo simulations, illustrating under what conditions the proposed estimation procedures lead to improved results.

Cross-validation biases time series model selection even when used with VARs or with models that have martingale-like errors.

Addressing sample selection bias for machine learning methods (replication data)

Dylan Brewer and Alyssa Carlson

Accepted at Journal of Applied Econometrics, 2023

Overview

This replication package contains files required to reproduce results, tables, and figures using Matlab and Stata. We divide the project into instructions to replicate the simulation, the result from Huang et al (2006), and the application.

Simulation

For reproducing the simulation results

Included files in *\Simulation with short descriptions:

• SSML_simfunc: function that produces individual simulations runs
• SSML_simulation: script that loops over the SSML_simfunc for different DGP and multiple simulation runs
• SSML_figures: script that generates all figures for the paper
• SSML_compilefunc: function that compiles the results from SSML_simulation for the SSML_figures script

Steps for replicating simulation:

1. Save SSML_simfunc, SSML_simulation, SSML_figures, SSML_compilefunc to the same folder. This location will be referred to as the FILEPATH.
2. Create OUTPUT folder inside the FILEPATH location.
3. Change the FILEPATH location inside SSML_simulation and SSML_figures.
4. Run SSML_simulation to produce simulation data and results.
5. Run SSML_figures to produce figures.

Huang et al replication

For reproducing the Huang et. al. (2006) replication results.

Included files in *\HuangetalReplication with short descriptions:

• SSML_huangrep: script that replicates the results from Huang et. al. (2006)

Obtaining the dataset:

Go to https://archive.ics.uci.edu/dataset/14/breast+cancer and save file as "breast-cancer-wisconsin.data"

Steps for replicating results:

1. Save SSML_huangrep and the breast cancer data to the same folder. This location will be referred to as the FILEPATH.
2. Change the FILEPATH location inside SSML_huangrep
3. Run SSML_huangrep to produce results and figures.

Application

For reproducing the application section results.

Included program files in *\Application with short descriptions:

• G0_main_202308.do: Stata wrapper code that will run all application replication files
• G1_cqclean_202308.do: Cleans election outcomes data
• G2_cqopen_202308.do: Cleans open elections data
• G3_demographics_cainc30_202308.do: Cleans demographics data
• G4_fips_202308.do: Cleans FIPS code data
• G5_klarnerclean_202308.do: Cleans Klarner gubernatorial data
• G6_merge_202308.do: Merges cleaned datasets together
• G7_summary_202308.do: Generates summary statistics tables and figures
• G8_firststage_202308.do: Runs L1 penalized probit for the first stage
• G9_prediction_202308.m: Trains learners and makes predictions
• G10_figures_202308.m: Generates figures of prediction patterns
• G11_final_202308.do: Generates final figures and tables of results
• r1_lasso_alwayskeepCF_202308.do: Examines the effect of requiring the control function is not dropped from LASSO
• latexTable.m: Code by Eli Duenisch to write LaTeX tables from Matlab (https://www.mathworks.com/matlabcentral/fileexchange/44274-latextable)

Included non-confidential data in subdirectory `*\Application\Data`:

• \CAINC30: County level income and demographics data from the BEA
• \CPI: CPI data from the BLS
• \KlarnerGovernors: Carl Klarner's Governors Dataset available at https://dataverse.harvard.edu/dataset.xhtml?persistentId=hdl:1902.1/20408

Confidential data suppressed in subdirectory `*\Application\CD`:

These data cannot be transferred as part of the data use agreement with the CQ Press. Thus, the files are not included.

• \CQ_county: County level election outcomes available from http://library.cqpress.com/elections/login.php?requested=%2Felections%2Fdownload-data.php
• \CQ_open: Open elections available from http://library.cqpress.com/elections/advsearch/elections-with-open-seats-results.php?open_year1=1968&open_year2=2019&open_office=4

There is no batch download--downloads for each year must be done by hand. For each year, download as many state outcomes as possible and name the files YYYYa.csv, YYYYb.csv, etc. (Example: 1970a.csv, 1970b.csv, 1970c.csv, 1970d.csv). See line 18 of G1_cqclean_202308.do for file structure information.

Steps for replicating application:

1. Download confidential data from the CQ Press.
2. Change the working directory in G0_main_202308.do on line 18 to the application folder.
3. Change local matlabpath in G0_main_202308.do on line 18 to the appropriate location.
4. Set directory and file path in G9_prediction_202308.m and G10_figures_202308.m as necessary.
5. Run G0_main_202308.do in Stata to run all programs.
6. All output (figures and tables) will be saved to subdirectory *\Application\Output.

Contact

Contact Dylan Brewer (brewer@gatech.edu) or Alyssa Carlson (carlsonah@missouri.edu) for help with replication.

The Marketing Bias dataset encapsulates the interactions between users and products on ModCloth and Amazon Electronics, emphasizing on the potential marketing bias inherent in product recommendations. This bias is explored through attributes related to product marketing and user/item interactions.

Basic Statistics:
- ModCloth: - Reviews: 99,893 - Items: 1,020 - Users: 44,783 - Bias Type: Body Shape

• Amazon Electronics:
  • Reviews: 1,292,954
  • Items: 9,560
  • Users: 1,157,633
  • Bias Type: Gender

Metadata: - Ratings - Product Images - User Identities - Item Sizes, User Genders

Example (ModCloth): The data example provided showcases a snippet from ModCloth data with columns like item_id, user_id, rating, timestamp, size, fit, user_attr, model_attr, and others.

Download Links: Visit the project page for download links.

Citation: If you utilize this dataset, please cite the following:

Title: Addressing Marketing Bias in Product Recommendations Authors: Mengting Wan, Jianmo Ni, Rishabh Misra, Julian McAuley Published In: WSDM, 2020 PDF Link

Dataset Files: - df_electronics.csv - df_modcloth.csv

The dataset is structured to provide a comprehensive overview of user-item interactions and attributes that may contribute to marketing bias, making it a valuable resource for anyone investigating marketing strategies and recommendation systems.

An online search of government websites and published literature was performed for regional data reports on COVID-19 cases that included sex as a variable from 1st January 2020 up until 1st June 2020 (Search terms: COVID-19/case/sex/country/data/death/ICU/ITU). In order to ensure unbiased representation from as many regions as possible, a cross check was done using the list of countries reporting data on ‘Worldometer’, and an attempt was made to include as many regions reporting sex data as possible. Reports were translated using Google translate if they were not in English.Data selection, extraction and synthesisReports were included if they contained sex as a variable in data describing case number, intensive treatment unit (ITU) admission, or mortality. Data were entered directly by individual researchers into an online structured data extraction table. For some sources, counts of male confirmed cases or male deaths were not provided, but percentages of male cases or male deaths were provided instead. To include these sources and avoid biases that might be introduced by their exclusion, we calculated counts of male confirmed cases and male deaths from the reported percentages with rounding to the nearest integer. We acknowledge that this approach assumes that the reported percentages are reflective of the true percentages. For some sources, data included confirmed cases and deaths of unknown sex. For these sources, the reported totals were used where the proportion of unknown sex was small. This approach was preferred to excluding cases of unknown sex in order to avoid bias. The estimates represent the proportion of known male infections and odds ratios for mortality associated with known male sex, and will differ slightly from what the true values would be if the sex had been reported for all cases. Data were available at the level of country or regional summary data representing distinct individuals for each report, but not at the level of covariates for all individuals within a study. Consequently, covariates such as lifestyle, comorbidities, testing method and case type (hospital vs. community) could not be controlled for.

IInformation sampling is often biased towards seeking evidence that confirms one’s prior beliefs. Despite such biases being a pervasive feature of human behavior, their underlying causes remain unclear. Many accounts of these biases appeal to limitations of human hypothesis testing and cognition, de facto evoking notions of bounded rationality, but neglect more basic aspects of behavioral control. Here, we investigated a potential role for Pavlovian approach in biasing which information humans will choose to sample. We collected a large novel dataset from 32,445 human subjects, making over 3 million decisions, who played a gambling task designed to measure the latent causes and extent of information-sampling biases. We identified three novel approach-related biases, formalized by comparing subject behavior to a dynamic programming model of optimal information gathering. These biases reflected the amount of information sampled (“positive evidence approach”), the selection of which information to sample (“sampling the favorite”), and the interaction between information sampling and subsequent choices (“rejecting unsampled options”). The prevalence of all three biases was related to a Pavlovian approach-avoid parameter quantified within an entirely independent economic decision task. Our large dataset also revealed that individual differences in the amount of information gathered are a stable trait across multiple gameplays and can be related to demographic measures, including age and educational attainment. As well as revealing limitations in cognitive processing, our findings suggest information sampling biases reflect the expression of primitive, yet potentially ecologically adaptive, behavioral repertoires. One such behavior is sampling from options that will eventually be chosen, even when other sources of information are more pertinent for guiding future action.

Data S1R script for simulations. Simulations of fixed- and random-effects meta-analysis using alternative estimators: one-sample mean, two-sample Hedges' g, and two-sample lnR, for comparison of performance by inverse-variance weighting and inverse-adjusted-variance weighting.Doncaster&Spake_Data_S1.txtData S2R script for calculating mean-adjusted error variance. Finds the mean-adjusted study variance for all the primary studies contributing to a meta-analysis, for a one-sample mean, or two-sample log response ratio, or or two-sample Hedges' g.Doncaster&Spake_Data_S2.txt,1. Meta-analyses conventionally weight study estimates on the inverse of their error variance, in order to maximize precision. Unbiased variability in the estimates of these study-level error variances increases with the inverse of study-level replication. Here we demonstrate how this variability accumulates asymmetrically across studies in precision-weighted meta-analysis, to cause undervaluation of the meta-level effect size or its error variance (the meta-effect and meta-variance). 2. Small samples, typical of the ecological literature, induce big sampling errors in variance estimation, which substantially bias precision-weighted meta-analysis. Simulations revealed that biases differed little between random- and fixed-effects tests. Meta-estimation of a one-sample mean from 20 studies, with sample sizes of 3 to 20 observations, undervalued the meta-variance by ~20%. Meta-analysis of two-sample designs from 20 studies, with sample sizes of 3 to 10 observations, undervalued the meta-variance by 15-20% for the log response ratio (lnR); it undervalued the meta-effect by ~10% for the standardised mean difference (SMD). 3. For all estimators, biases were eliminated or reduced by a simple adjustment to the weighting on study precision. The study-specific component of error variance prone to sampling error and not parametrically attributable to study-specific replication was replaced by its cross-study mean, on the assumption of random sampling from the same population variance for all studies, and sufficient studies for averaging. Weighting each study by the inverse of this mean-adjusted error variance universally improved accuracy in estimation of both the meta-effect and its significance, regardless of number of studies. For comparison, weighting only on sample size gave the same improvement in accuracy, but could not sensibly estimate significance. 4. For the one-sample mean and two-sample lnR, adjusted weighting also improved estimation of between-study variance by DerSimonian-Laird and REML methods. For random-effects meta-analysis of SMD from little-replicated studies, the most accurate meta-estimates obtained from adjusted weights following conventionally-weighted estimation of between-study variance. 5. We recommend adoption of weighting by inverse adjusted-variance for meta-analyses of well- and little-replicated studies, because it improves accuracy and significance of meta-estimates, and it can extend the scope of the meta-analysis to include some studies without variance estimates.

We measured browsing and height of young aspen (≥ 1 year-old) in 113 plots distributed randomly across the study area (Fig. 1). Each plot was a 1 × 20 m belt transect located randomly within an aspen stand that was itself randomly selected from an inventory of stands with respect to high and low wolf-use areas (Ripple et al. 2001). The inventory was a list of 992 grid cells (240 × 360 m) that contained at least one stand (Appendix S1). A “stand” was a group of tree-size aspen (>10 cm diameter at breast height) in which each tree was ≤ 30 m from every other tree. One hundred and thirteen grid cells were randomly selected from the inventory (~11% of 992 cells), one stand was randomly selected from each cell, and one plot was randomly established in each stand. Each plot likely represented a genetically-independent sample (Appendix S1).

We measured aspen at the end of the growing season (late July to September), focusing on plants ≤ 600 cm tall, which we termed “young aspen.” For each ...

Advances in neuroimaging, genomic, motion tracking, eye-tracking and many other technology-based data collection methods have led to a torrent of high dimensional datasets, which commonly have a small number of samples because of the intrinsic high cost of data collection involving human participants. High dimensional data with a small number of samples is of critical importance for identifying biomarkers and conducting feasibility and pilot work, however it can lead to biased machine learning (ML) performance estimates. Our review of studies which have applied ML to predict autistic from non-autistic individuals showed that small sample size is associated with higher reported classification accuracy. Thus, we have investigated whether this bias could be caused by the use of validation methods which do not sufficiently control overfitting. Our simulations show that K-fold Cross-Validation (CV) produces strongly biased performance estimates with small sample sizes, and the bias is still evident with sample size of 1000. Nested CV and train/test split approaches produce robust and unbiased performance estimates regardless of sample size. We also show that feature selection if performed on pooled training and testing data is contributing to bias considerably more than parameter tuning. In addition, the contribution to bias by data dimensionality, hyper-parameter space and number of CV folds was explored, and validation methods were compared with discriminable data. The results suggest how to design robust testing methodologies when working with small datasets and how to interpret the results of other studies based on what validation method was used.

Datasets from the study "An exploratory study of associations between judgement bias, demographic and behavioural characteristics, and detection task performance in medical detection dogs," including "Sample details" and "Judgement Bias Data" files. Complete download (zip, 33.7 KiB)

clinicaltrials.gov_searchThis is complete original dataset.identify completed trialsThis is the R script which when run on "clinicaltrials.gov_search.txt" will produce a .csv file which lists all the completed trials.FDA_table_with_sensThis is the final dataset after cross referencing the trials. An explanation of the variables is included in the supplementary file "2011-10-31 Prayle Hurley Smyth Supplementary file 3 variables in the dataset".analysis_after_FDA_categorization_and_sensThis R script reproduces the analysis from the paper, including the tables and statistical tests. The comments should make it self explanatory.2011-11-02 prayle hurley smyth supplementary file 1 STROBE checklistThis is a STROBE checklist for the study2011-10-31 Prayle Hurley Smyth Supplementary file 2 examples of categorizationThis is a supplementary file which illustrates some of the decisions which had to be made when categorizing trials.2011-10-31 Prayle Hurley Smyth Supplementary file 3 variables in th...

Sure! I'd be happy to provide you with an introduction to machine learning basics for beginners. Machine learning is a subfield of artificial intelligence (AI) that focuses on enabling computers to learn and make predictions or decisions without being explicitly programmed. Here are some key concepts and terms to help you get started:

1. Supervised Learning: In supervised learning, the machine learning algorithm learns from labeled training data. The training data consists of input examples and their corresponding correct output or target values. The algorithm learns to generalize from this data and make predictions or classify new, unseen examples.

2. Unsupervised Learning: Unsupervised learning involves learning patterns and relationships from unlabeled data. Unlike supervised learning, there are no target values provided. Instead, the algorithm aims to discover inherent structures or clusters in the data.

3. Training Data and Test Data: Machine learning models require a dataset to learn from. The dataset is typically split into two parts: the training data and the test data. The model learns from the training data, and the test data is used to evaluate its performance and generalization ability.

4. Features and Labels: In supervised learning, the input examples are often represented by features or attributes. For example, in a spam email classification task, features might include the presence of certain keywords or the length of the email. The corresponding output or target values are called labels, indicating the class or category to which the example belongs (e.g., spam or not spam).

5. Model Evaluation Metrics: To assess the performance of a machine learning model, various evaluation metrics are used. Common metrics include accuracy (the proportion of correctly predicted examples), precision (the proportion of true positives among all positive predictions), recall (the proportion of true positives predicted correctly), and F1 score (a combination of precision and recall).

6. Overfitting and Underfitting: Overfitting occurs when a model becomes too complex and learns to memorize the training data instead of generalizing well to unseen examples. On the other hand, underfitting happens when a model is too simple and fails to capture the underlying patterns in the data. Balancing the complexity of the model is crucial to achieve good generalization.

7. Feature Engineering: Feature engineering involves selecting or creating relevant features that can help improve the performance of a machine learning model. It often requires domain knowledge and creativity to transform raw data into a suitable representation that captures the important information.

8. Bias and Variance Trade-off: The bias-variance trade-off is a fundamental concept in machine learning. Bias refers to the errors introduced by the model's assumptions and simplifications, while variance refers to the model's sensitivity to small fluctuations in the training data. Reducing bias may increase variance and vice versa. Finding the right balance is important for building a well-performing model.

9. Supervised Learning Algorithms: There are various supervised learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), and neural networks. Each algorithm has its own strengths, weaknesses, and specific use cases.

10. Unsupervised Learning Algorithms: Unsupervised learning algorithms include clustering algorithms like k-means clustering and hierarchical clustering, dimensionality reduction techniques like principal component analysis (PCA) and t-SNE, and anomaly detection algorithms, among others.

These concepts provide a starting point for understanding the basics of machine learning. As you delve deeper, you can explore more advanced topics such as deep learning, reinforcement learning, and natural language processing. Remember to practice hands-on with real-world datasets to gain practical experience and further refine your skills.

Political science researchers have flexibility in how to analyze data, how to report data, and whether to report on data. Review of examples of reporting flexibility from the race and sex discrimination literature illustrates how research design choices can influence estimates and inferences. This reporting flexibility—coupled with the political imbalance among political scientists—creates the potential for political bias in reported political science estimates, but this potential for political bias can be reduced or eliminated through preregistration and preacceptance, in which researchers commit to a research design before completing data collection. Removing the potential for reporting flexibility can raise the credibility of political science research.

We want to build cooperative spaces where students can improve the quality of their work in iterative ways. However, are we sure that students will rate each other in unbiased ways. We will take some time to address implicit biases that we may have in an activity where we cultivate critical discussion and scientific skepticism to data visualization examples.

The estimation of disease prevalence in online search engine data (e.g., Google Flu Trends (GFT)) has received a considerable amount of scholarly and public attention in recent years. While the utility of search engine data for disease surveillance has been demonstrated, the scientific community still seeks ways to identify and reduce biases that are embedded in search engine data. The primary goal of this study is to explore new ways of improving the accuracy of disease prevalence estimations by combining traditional disease data with search engine data. A novel method, Biased Sentinel Hospital-based Area Disease Estimation (B-SHADE), is introduced to reduce search engine data bias from a geographical perspective. To monitor search trends on Hand, Foot and Mouth Disease (HFMD) in Guangdong Province, China, we tested our approach by selecting 11 keywords from the Baidu index platform, a Chinese big data analyst similar to GFT. The correlation between the number of real cases and the composite index was 0.8. After decomposing the composite index at the city level, we found that only 10 cities presented a correlation of close to 0.8 or higher. These cities were found to be more stable with respect to search volume, and they were selected as sample cities in order to estimate the search volume of the entire province. After the estimation, the correlation improved from 0.8 to 0.864. After fitting the revised search volume with historical cases, the mean absolute error was 11.19% lower than it was when the original search volume and historical cases were combined. To our knowledge, this is the first study to reduce search engine data bias levels through the use of rigorous spatial sampling strategies.

This study sought to inform various issues related to the extent of victims' adverse psychological and behavioral reactions to aggravated assault differentiated by the offenders' bias or non-bias motives. The goals of the research included (1) identifying the individual and situational factors related to bias- and non-bias-motivated aggravated assault, (2) determining the comparative severity and duration of psychological after-effects attributed to the victimization experience, and (3) measuring the comparative extent of behavioral avoidance strategies of victims. Data were collected on all 560 cases from the Boston Police Department's Community Disorders Unit from 1992 to 1997 that involved victim of a bias-motivated aggravated assault. In addition, data were collected on a 10-percent stratified random sample of victims of non-bias assaults within the city of Boston from 1993 to 1997, resulting in another 544 cases. For each of the cases, information was collected from each police incident report. Additionally, the researchers attempted to contact each victim in the sample to participate in a survey about their victimization experiences. The victim questionnaires included questions in five general categories: (1) incident information, (2) police response, (3) prosecutor response, (4) personal impact of the crime, and (5) respondent's personal characteristics. Criminal history variables were also collected regarding the number and type of adult and juvenile arrest charges against offenders and victims, as well as dispositions and arraignment dates.

In the analysis of causal effects in non-experimental studies, conditioning on observable covariates is one way to try to reduce unobserved confounder bias. However, a developing literature has shown that conditioning on certain covariates may increase bias, and the mechanisms underlying this phenomenon have not been fully explored. We add to the literature on bias-increasing covariates by first introducing a way to decompose omitted variable bias into three constituent parts: bias due to an unobserved confounder, bias due to excluding observed covariates, and bias due to amplification. This leads to two important findings. While instruments have been the primary focus of the bias amplification literature to date, we identify the fact that the popular approach of adding group fixed-effects can lead to bias amplification as well. This is an important finding because many practitioners think that fixed effects are convenient way to account for any and all group-level confounding and are at worst harmless. The second finding introduces the concept of bias unmasking and shows how it can be even more insidious than bias amplification in some cases. After introducing these new results analytically, we use constructed observational placebo studies to illustrate bias amplification and bias unmasking with real data. Finally, we propose a way to add bias decomposition information to graphical displays for sensitivity analysis to help practitioners think through the potential for bias amplification and bias unmasking in actual applications.

Bias Detection for AI Market Outlook

According to our latest research, the global Bias Detection for AI market size reached USD 1.27 billion in 2024, reflecting a rapidly maturing industry driven by mounting regulatory pressures and the demand for trustworthy AI systems. The market is projected to grow at a robust CAGR of 28.6% from 2025 to 2033, culminating in a forecasted market size of USD 11.16 billion by 2033. Growth in this sector is primarily fueled by the proliferation of AI applications across critical industries, increasing awareness of algorithmic fairness, and the escalating need for compliance with evolving global regulations.

A significant growth factor for the Bias Detection for AI market is the rising adoption of artificial intelligence and machine learning across diverse industry verticals, including BFSI, healthcare, retail, and government. As enterprises leverage AI to automate decision-making processes, the risk of embedding and amplifying biases inherent in training data or model architectures has become a major concern. This has led to increased investments in bias detection solutions, as organizations strive to ensure ethical AI deployment, protect brand reputation, and avoid costly regulatory penalties. Furthermore, the growing sophistication of AI models, such as deep learning and generative AI, has heightened the complexity of bias identification, necessitating advanced detection tools and services that can operate at scale and in real time.

Another key driver is the intensifying regulatory landscape surrounding AI ethics and accountability. Governments and regulatory bodies in North America, Europe, and Asia Pacific are introducing stringent guidelines mandating transparency, explainability, and fairness in AI systems. For example, the European Union’s AI Act and the United States’ Algorithmic Accountability Act are compelling organizations to implement robust bias detection frameworks as part of their compliance strategies. The threat of legal liabilities, coupled with the need to maintain consumer trust, is prompting enterprises to prioritize investment in bias detection technologies. This regulatory push is also fostering innovation among solution providers, resulting in a surge of new products and services tailored to specific industry requirements.

The increasing recognition of the business value of ethical AI is further accelerating market growth. Enterprises are now viewing bias detection not merely as a compliance requirement, but as a critical enabler of competitive differentiation. By proactively addressing bias, organizations can unlock new customer segments, enhance user experience, and drive innovation in product development. The integration of bias detection tools into AI development pipelines is also streamlining model validation and governance, reducing time-to-market for AI solutions while ensuring alignment with ethical standards. As a result, bias detection is becoming an integral component of enterprise AI strategies, driving sustained demand for both software and services in this market.

Regionally, North America is poised to maintain its dominance in the Bias Detection for AI market, owing to the presence of major technology vendors, proactive regulatory initiatives, and high AI adoption rates across industries. However, Asia Pacific is emerging as a high-growth region, fueled by rapid digital transformation, increasing regulatory scrutiny, and the expansion of AI research ecosystems in countries like China, Japan, and India. Europe, with its strong emphasis on data privacy and ethical AI, is also witnessing significant investments in bias detection solutions. The convergence of these regional dynamics is creating a vibrant global market landscape, characterized by diverse adoption patterns and evolving customer needs.

Component Analysis

The Bias Detection for AI market is segmented by component into software and services, each playing a pivotal role in addressing the multifaceted challenges of AI bias. The software segment acco

Results of single-cell and single-transcript measurements collected as part of the Bias and Resolvability Attribution using Split Samples (BRASS) study. Each file is a list of fluorescence intensities per cell (or estimated RNA counts per cell), for each of 12 different methods, for 3 biological replicates.

Aim: Citizen science is a cost-effective potential source of invasive species occurrence data. However, data quality issues due to unstructured sampling approaches may discourage the use of these observations by science and conservation professionals. This study explored the utility of low-structure iNaturalist citizen science data in invasive plant monitoring. We first examined the prevalence of invasive taxa in iNaturalist plant observations and sampling biases associated with those data. Using four invasive species as examples, we then compared iNaturalist and professional agency observations and used the two datasets to model suitable habitat for each species. Location: Hawaiʻi, USA Methods: To estimate the prevalence of invasive plant data, we compared the number of species and observations recorded in iNaturalist to botanical checklists for Hawaiʻi. Sampling bias was quantified along gradients of site accessibility, protective status, and vegetation disturbance using a bias index. Habitat suitability for four invasive species was modeled in Maxent, using observations from iNaturalist, professional agencies, and stratified subsets of iNaturalist data. Results: iNaturalist plant observations were biased toward invasive species, which were frequently recorded in areas with higher road/trail density and vegetation disturbance. Professional observations of four example invasive species tended to occur in less accessible, native-dominated sites. Habitat suitability models based on iNaturalist versus professional data showed moderate overlap and different distributions of suitable habitat across vegetation disturbance classes. Stratifying iNaturalist observations had little effect on how suitable habitat was distributed for the species modeled in this study. Main conclusions: Opportunistic iNaturalist observations have the potential to complement and expand professional invasive plant monitoring, which we found was often affected by inverse sampling biases. Invasive species represented a high proportion of iNaturalist plant observations, and were recorded in environments that were not captured by professional surveys. Combining the datasets thus led to more comprehensive estimates of suitable habitat.