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opencc-by-4.0Jun 2024View details →

Fire Weather Index for Europe from Downscaled and Bias-Corrected CMIP6 Model Outputs

This dataset contains the Canadian Forest Fire Weather Index (FWI) calculated from six downscaled and bias-corrected CMIP6 model outputs. The models included are: <ul> <li>ACCESS-CM2 (Ziehn et al. 2020)</li> <li>CanESM5 (Swart et al. 2019)</li> <li>CNRM-ESM2-1 (Séférian et al. 2019)</li> <li>EC-EARTH3 (EC-Earth Consortium 2019)</li> <li>MPI-ESM1-2-HR (von Storch et al. 2017)</li> <li>MRI-ESM2-0 (Yukimoto et al. 2019)</li> </ul> The dataset encompasses four Shared Socio-economic Pathway (SSP) projections: <ul> <li>SSP1-2.6</li> <li>SSP2-4.5</li> <li>SSP3-7.0</li> <li>SSP5-8.5</li> </ul> Each model output has been downscaled to a resolution of 0.0703135°, corresponding to approximately 9km×9km grids before the FWI calculation. The data covers Europe spatially and temporally spans from 1950 to 2080, offering comprehensive insights into past, present, and future fire weather conditions. This dataset supports the manuscript titled "The fire weather in Europe: large-scale trends towards higher danger" by Hetzer et al., currently under review in ERL. Detailed instructions for accessing the data can be found in the included README file.  Note: Downloads are password protected. Please use "FWI_2024" for access.   Funding: The authors acknowledge the financial support of the European Union’s Horizon 2020 research and innovation action for the FirEUrisk project under grant agreement ID: 101003890.

opencc-by-4.0May 2024View details →

MIdAS bias adjustment of extremes using Theil-Sen extrapolation: Data and plotting scripts for GMD-publication

When bias adjusting climate model data using quantile mapping approaches, one needs to prescribe what to do at the tails of the distribution, where a larger range of data is likely encountered outside the calibration period. The end results is highly dependent on the method used. For the current study, submitted to the journal Geoscientific Model Development, under the name 'Robust handling of extremes in quantile mapping - "Murder your darlings"' by Berg et al. (2024), the MIdAS bias adjustment method is evaluated and extended with additional functionality to deal with issues with bias adjustment of extreme precipitation. This entry contains data for the annual precipitation sums, and annual maximum daily precipitation, for a domain over Scandinavia, including a reference data set and a large ensemble of Euro-CORDEX regional climate models before and after bias adjustment using a range of experiments, as well as scripts for analysing the data and producing the figures of the paper. Further, the entry contains the daily timeseries for the reference and climate models needed to repeat all the experiments in the paper, along with the published code for MIdAS. The readme.txt documents provides a guide to structure the data, perform the experiments and to reproduce the plots of the paper.

opencc-by-4.0Feb 2024View details →

Assessing and Correcting Neighborhood Socioeconomic Spatial Sampling Biases in Citizen Science Mosquito Data Collection

Reporting data from the Mosquito Alert citizen science system, active catch basin surveillance, and mosquito trap surveillance used in "Assessing and Correcting Neighborhood Socioeconomic Spatial Sampling Biases in Citizen Science Mosquito Data Collection." The file named mosquito_alert_adult_bite_reports_Barcelona_2014_2023.Rds includes all adult mosquito and mosquito bite reports received from Barcelona Municipality from the start of the Mosqiuto Alert project in 2014 through the end of 2023. The file named mosquito_alert_validated_albopictus_reports_Barcelona_2014_23.Rds includes all expert-validated Ae. albopictus reports received from Barcelona Municipality during the same time period. The data is stored as RDS files and contain the following fields: <ul> <li>year - the year in which the report was made. Class = dbl.</li> <li>date - the date om which the report was made. Class = date.</li> <li>type - the report type, either adult mosquito ("adult") or mosquito breeding site ("site"). Class = chr.</li> <li>lon - the longitude of the report location. Class = dbl.</li> <li>lat - the latitude of the report location. Class = dbl.</li> <li>validation_score - Entolab validation score. Either 1 (possible Ae. albopictus) or 2 (probable Ae. albopictus). This field is present only in the validated reports data. </li> </ul> The file named active_catch_basin_drain_data.Rds includes information about all catch basin drains in Barcelona Municipality in which the Barcelona Public Health Agency (ASPB) detected mosquito activity as part of its continuous monitoring and control of mosquitoes from 2019 through 2023. The data is stored in an RDS file with the following fields: <ul> <li>any_reports - dummy variable indicating whether any Mosquito Alert adult mosquito or mosquito bite reports were sent through Mosquito Alert from within 200 m of the catch basin drain during the year in which the ASPB detected mosquito activity in hte catch basin drain. Class = lgl.</li> <li>se_expected - sampling effort for the 0.025 degree lon/lat sampling cell in which the catch basin drain lies during the year in which the ASPB detected mosquito activity in the drain. This value is taken from the SE_expected variable in the sampling_effort_daily_cellres_025.csv.gz file available at https://zenodo.org/records/12602985. Sampling effort is estimated as the expected number of participants sending at least one report from the cell during the day in question given the the number of participants recorded in the cell that day and the amount of time elapsed since each one began participating in the project. Class = dbl.</li> <li>p_singlehh - proportion of single-member households in the population of the census tract in which the catch basin drain is located. Class = dbl.</li> <li>mean_age - mean age of the population of the census tract in which the catch basin drain is located. Class = dbl.</li> <li>mean_rent_consumption_unit - mean income per consumption unit in the census tract in which the catch basin drain is located. Class = dbl.</li> <li>popd - population density of the census tract in which the catch basin drain is located. Class = dbl.</li> <li>id_item - unique identifier given to the catch basin drain. Drain itentifiers appear multiple times in the data when the ASPB detected activity in the drain in multiple years. Class = dbl.</li> </ul> The file named trap_data.Rds includes information on the adult mosquito trap surveillance analyzed in this article. The data is stored in an RDS file with the following fields: <ul> <li>females - number of Ae. albopictus females found in the trap. Class = dbl.</li> <li>trap_name - unique identifier for the trap. Class = chr.</li> <li>trapping_effort - number of days from when the trap was set to when it was checked. Class = dbl.</li> <li>date - date on which the trap was checked. Class = date.</li> <li>mean_tm30 - mean temperature for the 30 days leading up to the date on which the trap was checked. Class = dbl.</li> <li>mean_rent_consumption_unit - mean income per consumption unit for the census tract in which the trap was located. Class = dbl.</li> </ul>

opencc-by-4.0Feb 2024View details →

A bias in transsaccadic perception of spatial frequency changes

Dataset from the following publication: Sharvashidze, Hübner & Schütz, (2024). A bias in transsaccadic perception of spatial frequency changes. https://doi.org/10.1016/j.visres.2024.108453

opencc-by-sa-4.0May 2023View details →

Manifestations of Gender-Bias in ChatGPT 3.5: Differences in Responses Aimed at Young Adults

Eighteen pairs of prompt-response with different the LLM-based ChatGPT 3.5 chatbots exploring differences in responses aimed at young women vs young men, in the context of the STEM gap. Conversations were conducted in Spring 2023, exported as images and bundled in a zip file. The zip file also contains a spreadsheet with the text version of prompts and responses, as well as quantitative and qualitative comparative analysis.

opencc-by-4.0Jul 2024View details →

Examining the Influence of Political Bias on Large Language Model Performance in Stance Classification

Code and dataset for paper "Examining the Influence of Political Bias on Large Language Model Performance in Stance Classification". ICWSM 2025 Preprint: https://arxiv.org/abs/2407.17688 Citation:  @misc{ng2024examininginfluencepoliticalbias,       title={Examining the Influence of Political Bias on Large Language Model Performance in Stance Classification},        author={Lynnette Hui Xian Ng and Iain Cruickshank and Roy Ka-Wei Lee},       year={2024},       eprint={2407.17688},       archivePrefix={arXiv},       primaryClass={cs.CL},       url={https://arxiv.org/abs/2407.17688},  }

opencc-by-4.0Mar 2018View details →

The Role of Convective Gustiness in Reducing Seasonal Precipitation Biases in the Tropical West Pacific

Necessary outputs and scripts for recreating the figures for the journal article with the same title.

opencc-by-4.0Sep 2017View details →

Aggregated Spatial Data by province GPKG Format | Biodiversity Publication Bias Compromises Setting Conservation Priorities

Result of running https://github.com/raffael-hickisch/provincer

opennotspecifiedMar 2012View details →

Fig. 3 in A dragonfly in the desert: genetic pathways of the widespread Trithemis arteriosa (Odonata: Libellulidae) suggest male-biased dispersal

Fig. 3 Bayesian analysis of the nuclear genetic structure of T. arteriosa populations based on eight microsatellite loci. Each vertical bar represents an individual and is partitioned into one to three coloured segments indicating the individual membership in the three genetic

FIG. 4 in Nest-Site Fidelity and Sex-Biased Dispersal Affect Spatial Genetic Structure of Eastern Box Turtles (Terrapene carolina carolina) at Their Northern Range Edge

FIG. 4. Analysis of global structure along the river corridor from the first principal component of the sPCA represented by (A) interpolation of lagged principal scores showing genetic clines and (B) colors indicating individual scores. Coordinates have been deliberately omitted to deter poachers.

FIG. 3 in Nest-Site Fidelity and Sex-Biased Dispersal Affect Spatial Genetic Structure of Eastern Box Turtles (Terrapene carolina carolina) at Their Northern Range Edge

FIG. 3. Bubble plot showing the results of two-dimensional local spatial autocorrelation analysis for all Eastern Box Turtles (n ¼ 165) sampled in northwestern Michigan. Circles represent individuals and the size of the circle is proportional to the P-values from permutation testing, with large circles representing individuals that are significantly more related to their five nearest neighbors than expected (P, 0.05) based on a random distribution of genotypes. Figure shows the distribution of five genetic ''hotspots'' in relation to known nesting sites (NS) across the study area. Some distantly sampled individuals are omitted for figure clarity.

FIG. 2 in Nest-Site Fidelity and Sex-Biased Dispersal Affect Spatial Genetic Structure of Eastern Box Turtles (Terrapene carolina carolina) at Their Northern Range Edge

FIG. 2. Spatial genetic autocorrelograms of genetic correlation coefficients (r) as a function of distance for Eastern Box Turtles in northwestern Michigan. Plots represent (A) all individuals (n ¼ 165), (B) females only (n ¼ 104), and (C) males only (n ¼ 51). Dashed lines are permuted 95% confidence intervals across all data, and error bars are bootstrapped 95% confidence intervals within each distance class. Tables below graphs represent data for each distance class including the number of pairwise comparisons (n), the correlation coefficients (r), and the P-values (p) associated with bootstrap tests of significance for positive spatial genetic autocorrelation.

FIG. 1 in Nest-Site Fidelity and Sex-Biased Dispersal Affect Spatial Genetic Structure of Eastern Box Turtles (Terrapene carolina carolina) at Their Northern Range Edge

FIG. 1. Scatterplot showing the matrix of pairwise genetic distances and matrix of pairwise geographic distances for box turtles sampled along the river corridor. Warmer colors within the kernel density indicate higher densities of points. The line (slope ¼ 1.074727e–05; R2 ¼ 0.002992) shows the correlation trend.

opencc-by-4.0Aug 2024View details →

Implementation and Results of a "Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem"

1. Introduction In this archive, we provide the implementation and experimental results of a Randomized Local Search (RLS) with and without FFA applied to the Quadratic Assignment Problem. These are the data used in the paper below, which contains the exact specification of all algorithms, objective functions, and the encoding we applied. Jiayang Chen, Zhize Wu, Sarah L. Thomson, and Thomas Weise. Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem. In 16th International Conference on Evolutionary Computation Theory and Applications (ECTA'24), part of the 16th International Joint Conference on Computational Intelligence (IJCCI'24). November 20-24, 2024. Porto, Portugal. Setúbal, Portugal: SciTePress. To run the experiments, you need <a href="https://thomasweise.github.io/moptipyapps">moptipyapps</a> and <a href="https://thomasweise.github.io/moptipy">moptipy</a>, which contain the actual algorithm implementations. Both packages are available on GitHub and on PyPI. However, we include several versions of them in the folder <code>source/packages</code>, just in case. 2. Directory Structure This archive contains the following directories: <ul> <li><code>source</code> contains the Python source codes needed to run the experiment.</li> <li><code>source/packages</code> contains the source codes of the Python packages with the actual algorithm implementations.</li> <li><code>source/experiment_execution_scripts</code> contains the scripts to run the experiments.</li> <li><code>results</code> is the directory with the results, i.e., with the log files generated by the experiment.</li> <li><code>evaluator</code> is the folder containing Python scripts that were used to evaluate these results.</li> <li><code>evaluation</code> was generated using the evaluation scripts and contains tables and figures and a result summary in CSV format.</li> </ul> 3. License The files in this repository are under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a>, with the exception of the benchmark datasets included, which are under copyright of their respective owner (we believe that they are in the public domain, as they are provided by many sources, included in many software packages under various open source licenses, and on many websites). The license is contained as file <code>LICENSE.txt</code> in this archive. 4. Contact If you have any questions or suggestions, please contact Mr. Jiayang CHEN (陈嘉阳) of the Institute of Applied Optimization (应用优化研究所, <a href="http://iao.hfuu.edu.cn">IAO</a>) of the School of Artificial Intelligence and Big Data (<a href="http://www.hfuu.edu.cn/aibd/">人工智能与大数据学院</a>) at <a href="http://www.hfuu.edu.cn/english/">Hefei University</a> (<a href="http://www.hfuu.edu.cn/">合肥大学</a>) in Hefei, Anhui, China (中国安徽省合肥市) via email to <a href="mailto:cjy65820607@163.com">cjy65820607@163.com</a>.

opencc-by-4.0Sep 2024View details →

Datasets for "A classification-based approach to override cross-domain data bias in materials discovery"

This repository provides the featurized versions of the specialized datasets, SuperCon and ESTM, utilized in the study titled 'Classification-based detection and quantification of cross-domain data bias in materials discovery'.

opencc-by-4.0Sep 2024View details →

Dataset: A Non-Stationary Bias Adjustment Method for improving the Inter-annual Variability and Persistence of Projected Precipitation

This dataset includes: (1) unbiased data using a novel non-stationary bias adjustment methodology specifically tailored for environmental variables exhibiting sporadic events characterized by substantial intensity variability; (2) the parameters of the non-stationary probability distributions using marinetools.temporal. The methodology involves establishing a probability threshold to adapt the occurrence of precipitation events and employing a non-stationary theoretical and parametric quantile mapping to adjust associated biases. The dataset is part of the results obtained after the application of the methodology to daily precipitation projections from seven regional climatic models of the RCP 8.5 scenario spanning 2006-2100, alongside historical concurrent data from projections and observations spanning 1970-2005. Its efficacy is compared a widely used quantile mapping method, revealing notable differences in the performance of the methods concerning the distribution of events throughout the year and the behaviour of mean and extreme intensity values. The proposed method demonstrates promising potential in reducing uncertainty associated with systematic errors in inter-annual precipitation variability. This bears significance in evaluating hydrological responses and its associated impacts particularly in semi-arid mountainous basins.

Figure 4 in Removal of historical taxonomic bias and its impact on biogeographic analyses: a case study of Neotropical tardigrade fauna

Figure 4. PCoA of the dissimilarity values for species compositions for each province from biogeographic regions and transitions zones (Andean region (AR), South American transition zone (SATZ), Neotropical region (NR), and Mexican transition zone (MTZ)) considering a. all data ('false cosmopolitan' and 'indigenous species'; n = 51 provinces) and b. only 'indigenous data' (n = 43 provinces). Points represent provinces. All provinces are connected to the centroid (larger points with black outline), representing the mean of ordination values from all provinces from that area. Points furthest from the rest are interconnected, forming a polygon representing the space occupied by that area in ordination space. The AR is represented by blue points, lines and polygon; SATZ by light green points, lines and polygon; NR by pink points, lines and polygon, and MTZ by purple points, lines and polygon.

Figure 3 in Removal of historical taxonomic bias and its impact on biogeographic analyses: a case study of Neotropical tardigrade fauna

Figure 3. Species accumulation curves for all data ('false cosmopolitan' and 'indigenous species'), and only 'indigenous data' for a. the Neotropical region (NR), b. the Andean region (AR), c. the South American transition zone (SATZ), and d. the Mexican transition zone (MTZ). Orange curves represent all data, violet curves represent only 'indigenous data', and shaded areas around them represent their 95% confidence interval. Each publication containing species records was considered a survey.

Figure 2 in Removal of historical taxonomic bias and its impact on biogeographic analyses: a case study of Neotropical tardigrade fauna

Figure 2. Map of all incidence records of freshwater and limnoterrestrial tardigrades, from 1908 to 2023, present in the Andean and Neotropical regions proposed by Morrone (2015) and Morrone et al. (2022), respectively. Orange circles with black outline represent records. The hierarchy of compartmentalisation is from highest to lowest level: region, transition zone, subregion, dominion, and province. Each level can be subdivided into multiple lower levels under their name (e.g., a region consisting of several subregions). Each biogeographic province is coloured according to its transition zone, subregion or dominion. The Neotropical region (NR), in this study, is composed of the Antillean subregion (ASR), Brazilian subregion (BSR) and Chacoan subregion (CSR). The BSR is composed of the Mesoamerican dominion (MD), Pacific dominion (PD), Boreal Brazilian dominion (BBD) and South Brazilian dominion (BBD). The CSR is composed of the Southeastern Amazonian dominion (SAD), Chacoan dominion (CD) and Paraná dominion (PD). The Andean region (AR), in this study, is composed of the Central Chilean subregion (CCSR), Subantarctic subregion (SSR) and Patagonian subregion (PSR). The acronym for each region, transition zone, subregion or dominion is presented next to its name.

Figure 1 in Removal of historical taxonomic bias and its impact on biogeographic analyses: a case study of Neotropical tardigrade fauna

Figure 1. Biogeographic compartmentalisation of provinces from the Andean and Neotropical regions proposed by Morrone (2015) and Morrone et al. (2022), respectively. The hierarchy of compartmentalisation is from highest to lowest level: region, transition zone, subregion, dominion, and province. Each level can be subdivided into multiple lower levels under their name (e.g., a region consisting of several subregions). Each biogeographic province is coloured according to its transition zone, subregion or dominion. The Neotropical region (NR), in this study, is composed of the Antillean subregion (ASR), Brazilian subregion (BSR) and Chacoan subregion (CSR). The BSR is composed of the Mesoamerican dominion (MD), Pacific dominion (PD), Boreal Brazilian dominion (BBD) and South Brazilian dominion (BBD). The CSR is composed of the Southeastern Amazonian dominion (SAD), Chacoan dominion (CD) and Paraná dominion (PD). The Andean region (AR), in this study, is composed of the Central Chilean subregion (CCSR), Subantarctic subregion (SSR) and Patagonian subregion (PSR). The acronym for each region, transition zone, subregion or dominion is presented next to its name. Under each subregion or dominion, the names of the comprising provinces are listed. The nature of the records present in each province is indicated by an icon of a coloured tardigrade next to its name: provinces without records of tardigrade species (red tardigrade), provinces with only records of 'false cosmopolitan species' (blue tardigrade), provinces with records of 'false cosmopolitan' and 'indigenous species' (orange tardigrade), and provinces with only records of 'indigenous species' (violet tardigrade). The tardigrade icon is in the public domain and was obtained from Phylopic (https://www.phylopic.org).