The results represent experiments on four datasets based on 20 simulated experiments. The proposed method (NewAlgo) produces the best overall results.

The Multi-Domain Outlier Detection Dataset contains datasets for conducting outlier detection experiments for four different application domains:

1. Astrophysics - detecting anomalous observations in the Dark Energy Survey (DES) catalog (data type: feature vectors)
2. Planetary science - selecting novel geologic targets for follow-up observation onboard the Mars Science Laboratory (MSL) rover (data type: grayscale images)
3. Earth science: detecting anomalous samples in satellite time series corresponding to ground-truth observations of maize crops (data type: time series/feature vectors)
4. Fashion-MNIST/MNIST: benchmark task to detect anomalous MNIST images among Fashion-MNIST images (data type: grayscale images)

Each dataset contains a "fit" dataset (used for fitting or training outlier detection models), a "score" dataset (used for scoring samples used to evaluate model performance, analogous to test set), and a label dataset (indicates whether samples in the score dataset are considered outliers or not in the domain of each dataset).

To read more about the datasets and how they are used for outlier detection, or to cite this dataset in your own work, please see the following citation:

Kerner, H. R., Rebbapragada, U., Wagstaff, K. L., Lu, S., Dubayah, B., Huff, E., Lee, J., Raman, V., and Kulshrestha, S. (2022). Domain-agnostic Outlier Ranking Algorithms (DORA)-A Configurable Pipeline for Facilitating Outlier Detection in Scientific Datasets. Under review for Frontiers in Astronomy and Space Sciences.

The presence of outliers in response times can affect statistical analyses and lead to incorrect interpretation of the outcome of a study. Therefore, it is a widely accepted practice to try to minimize the effect of outliers by preprocessing the raw data. There exist numerous methods for handling outliers and researchers are free to choose among them. In this article, we use computer simulations to show that serious problems arise from this flexibility. Choosing between alternative ways for handling outliers can result in the inflation of p-values and the distortion of confidence intervals and measures of effect size. Using Bayesian parameter estimation and probability distributions with heavier tails eliminates the need to deal with response times outliers, but at the expense of opening another source of flexibility.

The following report outlines the workflow used to optimize your Find Outliers result:Initial Data Assessment.There were 137 valid input features.There were 4 outlier locations; these will not be used to compute the polygon cell size.Incident AggregationThe polygon cell size was 49251.0000 Meters.The aggregation process resulted in 72 weighted areas.Incident Count Properties:Min1.0000Max21.0000Mean1.9028Std. Dev.2.4561Scale of AnalysisThe optimal fixed distance band selected was based on peak clustering found at 94199.9365 Meters.Outlier AnalysisCreating the random reference distribution with 499 permutations.There are 3 output features statistically significant based on a FDR correction for multiple testing and spatial dependence.There are 2 statistically significant high outlier features.There are 0 statistically significant low outlier features.There are 0 features part of statistically significant low clusters.There are 1 features part of statistically significant high clusters.OutputPink output features are part of a cluster of high values.Light Blue output features are part of a cluster of low values.Red output features represent high outliers within a cluster of low values.Blue output features represent low outliers within a cluster of high values.

The following report outlines the workflow used to optimize your Find Outliers result:Initial Data Assessment.There were 721 valid input features.GRM Properties:Min0.0000Max157.0200Mean9.1692Std. Dev.8.4220There were 4 outlier locations; these will not be used to compute the optimal fixed distance band.Scale of AnalysisThe optimal fixed distance band selected was based on peak clustering found at 1894.5039 Meters.Outlier AnalysisCreating the random reference distribution with 499 permutations.There are 248 output features statistically significant based on a FDR correction for multiple testing and spatial dependence.There are 30 statistically significant high outlier features.There are 7 statistically significant low outlier features.There are 202 features part of statistically significant low clusters.There are 9 features part of statistically significant high clusters.OutputPink output features are part of a cluster of high GRM values.Light Blue output features are part of a cluster of low GRM values.Red output features represent high outliers within a cluster of low GRM values.Blue output features represent low outliers within a cluster of high GRM values.

These data sets were originally created for the following publications:

M. E. Houle, H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek
Can Shared-Neighbor Distances Defeat the Curse of Dimensionality?
In Proceedings of the 22nd International Conference on Scientific and Statistical Database Management (SSDBM), Heidelberg, Germany, 2010.

H.-P. Kriegel, E. Schubert, A. Zimek
Evaluation of Multiple Clustering Solutions
In 2nd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with ECML PKDD 2011, Athens, Greece, 2011.

The outlier data set versions were introduced in:

E. Schubert, R. Wojdanowski, A. Zimek, H.-P. Kriegel
On Evaluation of Outlier Rankings and Outlier Scores
In Proceedings of the 12th SIAM International Conference on Data Mining (SDM), Anaheim, CA, 2012.

They are derived from the original image data available at https://aloi.science.uva.nl/

The image acquisition process is documented in the original ALOI work: J. M. Geusebroek, G. J. Burghouts, and A. W. M. Smeulders, The Amsterdam library of object images, Int. J. Comput. Vision, 61(1), 103-112, January, 2005

Additional information is available at: https://elki-project.github.io/datasets/multi_view

The following views are currently available:

Feature type	Description	Files
Object number	Sparse 1000 dimensional vectors that give the true object assignment	objs.arff.gz
RGB color histograms	Standard RGB color histograms (uniform binning)	aloi-8d.csv.gz aloi-27d.csv.gz aloi-64d.csv.gz aloi-125d.csv.gz aloi-216d.csv.gz aloi-343d.csv.gz aloi-512d.csv.gz aloi-729d.csv.gz aloi-1000d.csv.gz
HSV color histograms	Standard HSV/HSB color histograms in various binnings	aloi-hsb-2x2x2.csv.gz aloi-hsb-3x3x3.csv.gz aloi-hsb-4x4x4.csv.gz aloi-hsb-5x5x5.csv.gz aloi-hsb-6x6x6.csv.gz aloi-hsb-7x7x7.csv.gz aloi-hsb-7x2x2.csv.gz aloi-hsb-7x3x3.csv.gz aloi-hsb-14x3x3.csv.gz aloi-hsb-8x4x4.csv.gz aloi-hsb-9x5x5.csv.gz aloi-hsb-13x4x4.csv.gz aloi-hsb-14x5x5.csv.gz aloi-hsb-10x6x6.csv.gz aloi-hsb-14x6x6.csv.gz
Color similiarity	Average similarity to 77 reference colors (not histograms) 18 colors x 2 sat x 2 bri + 5 grey values (incl. white, black)	aloi-colorsim77.arff.gz (feature subsets are meaningful here, as these features are computed independently of each other)
Haralick features	First 13 Haralick features (radius 1 pixel)	aloi-haralick-1.csv.gz
Front to back	Vectors representing front face vs. back faces of individual objects	front.arff.gz
Basic light	Vectors indicating basic light situations	light.arff.gz
Manual annotations	Manually annotated object groups of semantically related objects such as cups	manual1.arff.gz

Outlier Detection Versions

Additionally, we generated a number of subsets for outlier detection:

Feature type	Description	Files
RGB Histograms	Downsampled to 100000 objects (553 outliers)	aloi-27d-100000-max10-tot553.csv.gz aloi-64d-100000-max10-tot553.csv.gz
	Downsampled to 75000 objects (717 outliers)	aloi-27d-75000-max4-tot717.csv.gz aloi-64d-75000-max4-tot717.csv.gz
	Downsampled to 50000 objects (1508 outliers)	aloi-27d-50000-max5-tot1508.csv.gz aloi-64d-50000-max5-tot1508.csv.gz

Verbal and Quantitative Reasoning GRE scores and percentiles were collected by querying the student database for the appropriate information. Any student records that were missing data such as GRE scores or grade point average were removed from the study before the data were analyzed. The GRE Scores of entering doctoral students from 2007-2012 were collected and analyzed. A total of 528 student records were reviewed. Ninety-six records were removed from the data because of a lack of GRE scores. Thirty-nine of these records belonged to MD/PhD applicants who were not required to take the GRE to be reviewed for admission. Fifty-seven more records were removed because they did not have an admissions committee score in the database. After 2011, the GRE’s scoring system was changed from a scale of 200-800 points per section to 130-170 points per section. As a result, 12 more records were removed because their scores were representative of the new scoring system and therefore were not able to be compared to the older scores based on raw score. After removal of these 96 records from our analyses, a total of 420 student records remained which included students that were currently enrolled, left the doctoral program without a degree, or left the doctoral program with an MS degree. To maintain consistency in the participants, we removed 100 additional records so that our analyses only considered students that had graduated with a doctoral degree. In addition, thirty-nine admissions scores were identified as outliers by statistical analysis software and removed for a final data set of 286 (see Outliers below). Outliers We used the automated ROUT method included in the PRISM software to test the data for the presence of outliers which could skew our data. The false discovery rate for outlier detection (Q) was set to 1%. After removing the 96 students without a GRE score, 432 students were reviewed for the presence of outliers. ROUT detected 39 outliers that were removed before statistical analysis was performed. Sample See detailed description in the Participants section. Linear regression analysis was used to examine potential trends between GRE scores, GRE percentiles, normalized admissions scores or GPA and outcomes between selected student groups. The D’Agostino & Pearson omnibus and Shapiro-Wilk normality tests were used to test for normality regarding outcomes in the sample. The Pearson correlation coefficient was calculated to determine the relationship between GRE scores, GRE percentiles, admissions scores or GPA (undergraduate and graduate) and time to degree. Candidacy exam results were divided into students who either passed or failed the exam. A Mann-Whitney test was then used to test for statistically significant differences between mean GRE scores, percentiles, and undergraduate GPA and candidacy exam results. Other variables were also observed such as gender, race, ethnicity, and citizenship status within the samples. Predictive Metrics. The input variables used in this study were GPA and scores and percentiles of applicants on both the Quantitative and Verbal Reasoning GRE sections. GRE scores and percentiles were examined to normalize variances that could occur between tests. Performance Metrics. The output variables used in the statistical analyses of each data set were either the amount of time it took for each student to earn their doctoral degree, or the student’s candidacy examination result.

The following report outlines the workflow used to optimize your Find Outliers result:Initial Data Assessment.There were 1684 valid input features.POVERTY Properties:Min0.0000Max91.8000Mean18.9902Std. Dev.12.7152There were 22 outlier locations; these will not be used to compute the optimal fixed distance band.Scale of AnalysisThe optimal fixed distance band was based on the average distance to 30 nearest neighbors: 3709.0000 Meters.Outlier AnalysisCreating the random reference distribution with 499 permutations.There are 1155 output features statistically significant based on a FDR correction for multiple testing and spatial dependence.There are 68 statistically significant high outlier features.There are 84 statistically significant low outlier features.There are 557 features part of statistically significant low clusters.There are 446 features part of statistically significant high clusters.OutputPink output features are part of a cluster of high POVERTY values.Light Blue output features are part of a cluster of low POVERTY values.Red output features represent high outliers within a cluster of low POVERTY values.Blue output features represent low outliers within a cluster of high POVERTY values.

State politics researchers commonly employ ordinary least squares (OLS) regression or one of its variants to test linear hypotheses. However, OLS is easily influenced by outliers and thus can produce misleading results when the error term distribution has heavy tails. Here we demonstrate that median regression (MR), an alternative to OLS that conditions the median of the dependent variable (rather than the mean) on the independent variables, can be a solution to this problem. Then we propose and validate a hypothesis test that applied researchers can use to select between OLS and MR in a given sample of data. Finally, we present two examples from state politics research in which (1) the test selects MR over OLS and (2) differences in results between the two methods could lead to different substantive inferences. We conclude that MR and the test we propose can improve linear models in state politics research.

Contains the simulated data and Stata code used to produce the results for the manuscript titled "Evaluating methods of outlier detection when benchmarking clinical registry data – a simulation study", accepted for publication in the Health Services and Outcomes Research Methodology Journal.

data_files.zip (code to generate all files in "do_files\simstudy1_preparation.do"):
raw_data - the .dta files produced from running the user-written hiersim command (https://doi.org/10.26180/24480889.v1)
summary_data - the .dta files produced from summarising of the results across each unique simulated scenario and method combination (performance measure average and 95% Monte Carlo confidence intervals)
parameter_check - the .dta files produced from summarising the simulated data parameters across each unique simulated scenario (performance measure average and 95% Monte Carlo confidence intervals)

do_files.zip:
simstudy1_preparation.do - the code to run the simulations (using the hiersim command, available at https://doi.org/10.26180/24480889.v1) and create summary datasets (performance measures and parameter checks)
simstudy1_manuscript.do - the code to produce the figures included in the main manuscript
simstudy1_supplementary.do - the code to produce the table and figures included in the manuscript supplementary material

This dataset (Auction Verification Regression Anomalies) contains the anomaly scores of residual outliers obtained from a regression tree constructed from the Auction Verification dataset available at: https://archive.ics.uci.edu/dataset/713/auction+verification (Ordoni et al., 2022).

The Auction Verification Regression Anomalies dataset contains three columns. - First column: RecordNum, which represents the sequence of records in the Auction Verification dataset. - Second column: contains the respective anomaly scores of each column. - Third column: contains the outlier labels, where anomaly scores > 2 are labelled as "Yes", else "No".

To make sense of this Auction Verification Regression Anomalies dataset, reading the problem context described in the paper written by Ordoni et al. (2022) will be helpful. In short, one of the tasks of Ordoni et al. was to facilitate the auction verification process by predicting auction verification time. However, there might be times when the predictions are inaccurate.

The dataset (Auction Verification Regression Anomalies) contains information on whether the auction verification time of the original data points (in Auction Verification) was accurately predicted. Data points with inaccurate predictions would be labelled as "Yes" in the outlier labels.

How to use Merge the anomaly scores and labels with the Auction Verification dataset available at: https://archive.ics.uci.edu/dataset/713/auction+verification (Ordoni et al., 2022). Please use the exact record order. That is, RecordNum 1, 2, 3, ... corresponds to the first, second, third, ... record in the Auction Verification dataset, respectively.

This dataset has been used in the following research papers: (1) Title: Enhancing Regression Tree Predictions with Terminal-Node Anomaly Detection Author: Swee Chuan Tan Published in: Proceedings of the 2023 6th Artificial Intelligence and Cloud Computing Conference (AICCC '23) Year: 2024 ISBN: 9798400716225 DOI: 10.1145/3639592.3639596 Publisher: Association for Computing Machinery Location: Kyoto, Japan Pages: 21–26 Keywords: Anomaly detection, Outlier, Regression tree, Robust regression Abstract: This paper presents a framework for detecting outliers in regression tree model predictions. It uses a training dataset to build a regression tree and labels significantly different predictions as outliers. These outlier labels are then used to construct an anomaly detector. The method significantly reduces the Mean Absolute Errors of regression tree predictions across four datasets, offering a practical approach for discarding suspicious predictions without compromising overall system quality. URL: https://doi.org/10.1145/3639592.3639596

(2) An enhanced version of the above article will appear in the Journal of Data Science and Intelligent Systems (JDSIS).

Reference: Ordoni, E., Bach, J., Fleck, A., & Bach, J. (2022). Auction Verification [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C52K6N.

Abstract: Building health management is an important part in running an efficient and cost-effective building. Many problems in a building’s system can go undetected for long periods of time, leading to expensive repairs or wasted resources. This project aims to help detect and diagnose the building‘s health with data driven methods throughout the day. Orca and IMS are two state of the art algorithms that observe an array of building health sensors and provide feedback on the overall system’s health as well as localize the problem to one, or possibly two, components. With this level of feedback the hope is to quickly identify problems and provide appropriate maintenance while reducing the number of complaints and service calls. Introduction: To prepare these technologies for the new installation, the proposed methods are being tested on a current system that behaves similarly to the future green building. Building 241 was determined to best resemble the proposed building 232 and therefore was chosen for this study. Building 241 is currently outfitted with 34 sensors that monitor the heating & cooling temperatures for the air and water systems as well as other various subsystem states. The daily sensor recordings were logged and sent to the IDU group for analysis. The period of analysis was focused from July 1st through August 10th 2009. Methodology: The two algorithms used for analysis were Orca and IMS. Both methods look for anomalies using a distanced based scoring approach. Orca has the ability to use a single data set and find outliers within that data set. This tactic was applied to each day. After scoring each time sample throughout a given day the Orca score profiles were compared by computing the correlation against all other days. Days with high overall correlations were considered normal however days with lower overall correlations were more anomalous. IMS, on the other hand, needs a normal set of data to build a model, which can be applied to a set of test data to asses how anomaly the particular data set is. The typical days identified by Orca were used as the reference/training set for IMS, while all the other days were passed through IMS resulting in an anomaly score profile for each day. The mean of the IMS score profile was then calculated for each day to produce a summary IMS score. These summary scores were ranked and the top outliers were identified (see Figure 1). Once the anomalies were identified the contributing parameters were then ranked by the algorithm. Analysis: The contributing parameters identified by IMS were localized to the return air temperature duct system. -7/03/09 (Figure 2 & 3) AHU-1 Return Air Temperature (RAT) Calculated Average Return Air Temperature -7/19/09 (Figure 3 & 4) AHU-2 Return Air Temperature (RAT) Calculated Average Return Air Temperature IMS identified significantly higher temperatures compared to other days during the month of July and August. Conclusion: The proposed algorithms Orca and IMS have shown that they were able to pick up significant anomalies in the building system as well as diagnose the anomaly by identifying the sensor values that were anomalous. In the future these methods can be used on live streaming data and produce a real time anomaly score to help building maintenance with detection and diagnosis of problems.

The scores of the (true) cheating cases and the outlier cases determined by the detection methods in DS2 dataset.

ABSTRACT The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.

Here are six files that provide details for all 44,120 identified single nucleotide polymorphisms (SNPs) or the 215 outlier SNPs associated with the evolution of rapid character displacement among replicate islands with (2Spp) and without competition (1Spp) between two Anolis species. On 2Spp islands, A. carolinensis occurs higher in trees and have evolved larger toe pads. Among 1Spp and 2Spp island populations, we identify 44,120 SNPs, with 215-outlier SNPs with improbably large FST values, low nucleotide variation, greater linkage than expected, and these SNPs are enriched for animal walking behavior. Thus, we conclude that these 215-outliers are evolving by natural selection in response to the phenotypic convergent evolution of character displacement. There are two, non-mutually exclusive perspective of these nucleotide variants. One is character displacement is convergent: all 215 outlier SNPs are shared among 3 out of 5 2Spp island and 24% of outlier SNPS are shared among all five out of five 2Spp island. Second, character displacement is genetically redundant because the allele frequencies in one or more 2Spp are similar to 1Spp islands: among one or more 2Spp islands 33% of outlier SNPS are within the range of 1Spp MiAF and 76% of outliers are more similar to 1Spp island than mean MiAF of 2Spp islands. Focusing on convergence SNP is scientifically more robust, yet it distracts from the perspective of multiple genetic solutions that enhances the rate and stability of adaptive change. The six files include: a description of eight islands, details of 94 individuals, and four files on SNPs. The four SNP files include the VCF files for 94 individuals with 44KSNPs and two files (Excel sheet/tab-delimited file) with FST, p-values and outlier status for all 44,120 identified single nucleotide polymorphisms (SNPs) associated with the evolution of rapid character displacement. The sixth file is a detailed file on the 215 outlier SNPs. Complete sequence data is available at Bioproject PRJNA833453, which including samples not included in this study. The 94 individuals used in this study are described in “Supplemental_Sample_description.txt” Methods Anoles and genomic DNA: Tissue or DNA for 160 Anolis carolinensis and 20 A. sagrei samples were provided by the Museum of Comparative Zoology at Harvard University (Table S2). Samples were previously used to examine evolution of character displacement in native A. carolinensis following invasion by A. sagrei onto man-made spoil islands in Mosquito Lagoon Florida (Stuart et al. 2014). One hundred samples were genomic DNAs, and 80 samples were tissues (terminal tail clip, Table S2). Genomic DNA was isolated from 80 of 160 A. carolinensis individuals (MCZ, Table S2) using a custom SPRI magnetic bead protocol (Psifidi et al. 2015). Briefly, after removing ethanol, tissues were placed in 200 ul of GH buffer (25 mM Tris- HCl pH 7.5, 25 mM EDTA, , 2M GuHCl Guanidine hydrochloride, G3272 SIGMA, 5 mM CaCl2, 0.5% v/v Triton X-100, 1% N-Lauroyl-Sarcosine) with 5% per volume of 20 mg/ml proteinase K (10 ul/200 ul GH) and digested at 55º C for at least 2 hours. After proteinase K digestion, 100 ul of 0.1% carboxyl-modified Sera-Mag Magnetic beads (Fisher Scientific) resuspended in 2.5 M NaCl, 20% PEG were added and allowed to bind the DNA. Beads were subsequently magnetized and washed twice with 200 ul 70% EtOH, and then DNA was eluted in 100 ul 0.1x TE (10 mM Tris, 0.1 mM EDTA). All DNA samples were gel electrophoresed to ensure high molecular mass and quantified by spectrophotometry and fluorescence using Biotium AccuBlueTM High Sensitivity dsDNA Quantitative Solution according to manufacturer’s instructions. Genotyping-by-sequencing (GBS) libraries were prepared using a modified protocol after Elshire et al. (Elshire et al. 2011). Briefly, high-molecular-weight genomic DNA was aliquoted and digested using ApeKI restriction enzyme. Digests from each individual sample were uniquely barcoded, pooled, and size selected to yield insert sizes between 300-700 bp (Borgstrom et al. 2011). Pooled libraries were PCR amplified (15 cycles) using custom primers that extend into the genomic DNA insert by 3 bases (CTG). Adding 3 extra base pairs systematically reduces the number of sequenced GBS tags, ensuring sufficient sequencing depth. The final library had a mean size of 424 bp ranging from 188 to 700 bp . Anolis SNPs: Pooled libraries were sequenced on one lane on the Illumina HiSeq 4000 in 2x150 bp paired-end configuration, yielding approximately 459 million paired-end reads ( ~138 Gb). The medium Q-Score was 42 with the lower 10% Q-Scores exceeding 32 for all 150 bp. The initial library contained 180 individuals with 8,561,493 polymorphic sites. Twenty individuals were Anolis sagrei, and two individuals (Yan 1610 & Yin 1411) clustered with A. sagrei and were not used to define A. carolinesis’ SNPs. Anolis carolinesis reads were aligned to the Anolis carolinensis genome (NCBI RefSeq accession number:/GCF_000090745.1_AnoCar2.0). Single nucleotide polymorphisms (SNPs) for A. carolinensis were called using the GBeaSy analysis pipeline (Wickland et al. 2017) with the following filter settings: minimum read length of 100 bp after barcode and adapter trimming, minimum phred-scaled variant quality of 30 and minimum read depth of 5. SNPs were further filtered by requiring SNPs to occur in > 50% of individuals, and 66 individuals were removed because they had less than 70% of called SNPs. These filtering steps resulted in 51,155 SNPs among 94 individuals. Final filtering among 94 individuals required all sites to be polymorphic (with fewer individuals, some sites were no longer polymorphic) with a maximum of 2 alleles (all are bi-allelic), minimal allele frequency 0.05, and He that does not exceed HWE (FDR <0.01). SNPs with large He were removed (2,280 SNPs). These SNPs with large significant heterozygosity may result from aligning paralogues (different loci), and thus may not represent polymorphisms. No SNPs were removed with low He (due to possible demography or other exceptions to HWE). After filtering, 94 individual yielded 44,120 SNPs. Thus, the final filtered SNP data set was 44K SNPs from 94 indiviuals. Statistical Analyses: Eight A. carolinensis populations were analyzed: three populations from islands with native species only (1Spp islands) and 5 populations from islands where A. carolinesis co-exist with A. sagrei (2Spp islands, Table 1, Table S1). Most analyses pooled the three 1Spp islands and contrasted these with the pooled five 2Spp islands. Two approaches were used to define SNPs with unusually large allele frequency differences between 1Spp and 2Spp islands: 1) comparison of FST values to random permutations and 2) a modified FDIST approach to identify outlier SNPs with large and statistically unlikely FST values. Random Permutations: FST values were calculated in VCFTools (version 4.2, (Danecek et al. 2011)) where the p-value per SNP were defined by comparing FST values to 1,000 random permutations using a custom script (below). Basically, individuals and all their SNPs were randomly assigned to one of eight islands or to 1Spp versus 2Spp groups. The sample sizes (55 for 2Spp and 39 for 1Spp islands) were maintained. FST values were re-calculated for each 1,000 randomizations using VCFTools. Modified FDIST: To identify outlier SNPs with statistically large FST values, a modified FDIST (Beaumont and Nichols 1996) was implemented in Arlequin (Excoffier et al. 2005). This modified approach applies 50,000 coalescent simulations using hierarchical population structure, in which demes are arranged into k groups of d demes and in which migration rates between demes are different within and between groups. Unlike the finite island models, which have led to large frequencies of false positive because populations share different histories (Lotterhos and Whitlock 2014), the hierarchical island model avoids these false positives by avoiding the assumption of similar ancestry (Excoffier et al. 2009). References Beaumont, M. A. and R. A. Nichols. 1996. Evaluating loci for use in the genetic analysis of population structure. P Roy Soc B-Biol Sci 263:1619-1626. Borgstrom, E., S. Lundin, and J. Lundeberg. 2011. Large scale library generation for high throughput sequencing. PLoS One 6:e19119. Bradbury, P. J., Z. Zhang, D. E. Kroon, T. M. Casstevens, Y. Ramdoss, and E. S. Buckler. 2007. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633-2635. Cingolani, P., A. Platts, L. Wang le, M. Coon, T. Nguyen, L. Wang, S. J. Land, X. Lu, and D. M. Ruden. 2012. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6:80-92. Danecek, P., A. Auton, G. Abecasis, C. A. Albers, E. Banks, M. A. DePristo, R. E. Handsaker, G. Lunter, G. T. Marth, S. T. Sherry, G. McVean, R. Durbin, and G. Genomes Project Analysis. 2011. The variant call format and VCFtools. Bioinformatics 27:2156-2158. Earl, D. A. and B. M. vonHoldt. 2011. Structure Harvester: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genet Resour 4:359-361. Elshire, R. J., J. C. Glaubitz, Q. Sun, J. A. Poland, K. Kawamoto, E. S. Buckler, and S. E. Mitchell. 2011. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One 6:e19379. Evanno, G., S. Regnaut, and J. Goudet. 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611-2620. Excoffier, L., T. Hofer, and M. Foll. 2009. Detecting loci under selection in a hierarchically structured population. Heredity 103:285-298. Excoffier, L., G. Laval, and S. Schneider. 2005. Arlequin (version 3.0): An integrated software package for population genetics data analysis.

Abstract: Building health management is an important part in running an efficient and cost-effective building. Many problems in a building’s system can go undetected for long periods of time, leading to expensive repairs or wasted resources. This project aims to help detect and diagnose the building‘s health with data driven methods throughout the day. Orca and IMS are two state of the art algorithms that observe an array of building health sensors and provide feedback on the overall system’s health as well as localize the problem to one, or possibly two, components. With this level of feedback the hope is to quickly identify problems and provide appropriate maintenance while reducing the number of complaints and service calls. Introduction: To prepare these technologies for the new installation, the proposed methods are being tested on a current system that behaves similarly to the future green building. Building 241 was determined to best resemble the proposed building 232 and therefore was chosen for this study. Building 241 is currently outfitted with 34 sensors that monitor the heating & cooling temperatures for the air and water systems as well as other various subsystem states. The daily sensor recordings were logged and sent to the IDU group for analysis. The period of analysis was focused from July 1st through August 10th 2009. Methodology: The two algorithms used for analysis were Orca and IMS. Both methods look for anomalies using a distanced based scoring approach. Orca has the ability to use a single data set and find outliers within that data set. This tactic was applied to each day. After scoring each time sample throughout a given day the Orca score profiles were compared by computing the correlation against all other days. Days with high overall correlations were considered normal however days with lower overall correlations were more anomalous. IMS, on the other hand, needs a normal set of data to build a model, which can be applied to a set of test data to asses how anomaly the particular data set is. The typical days identified by Orca were used as the reference/training set for IMS, while all the other days were passed through IMS resulting in an anomaly score profile for each day. The mean of the IMS score profile was then calculated for each day to produce a summary IMS score. These summary scores were ranked and the top outliers were identified (see Figure 1). Once the anomalies were identified the contributing parameters were then ranked by the algorithm. Analysis: The contributing parameters identified by IMS were localized to the return air temperature duct system. -7/03/09 (Figure 2 & 3) AHU-1 Return Air Temperature (RAT) Calculated Average Return Air Temperature -7/19/09 (Figure 3 & 4) AHU-2 Return Air Temperature (RAT) Calculated Average Return Air Temperature IMS identified significantly higher temperatures compared to other days during the month of July and August. Conclusion: The proposed algorithms Orca and IMS have shown that they were able to pick up significant anomalies in the building system as well as diagnose the anomaly by identifying the sensor values that were anomalous. In the future these methods can be used on live streaming data and produce a real time anomaly score to help building maintenance with detection and diagnosis of problems.

Kurtosis of error distributions of MARS, GBR, KNN, and RFR for both datasets and both cases.

AbstractGenetics-based approaches have informed fisheries management for decades, yet remain challenging to implement within systems involving recently diverged stocks or where gene flow persists. In such cases, genetic markers exhibiting locus-specific (“outlier”) effects associated with divergent selection may provide promising alternatives to loci that reflect genome-wide (“neutral”) effects for guiding fisheries management. Okanagan Lake kokanee (Oncorhynchus nerka), a fishery of conservation concern, exhibits two sympatric ecotypes adapted to different reproductive environments, however, previous research demonstrated the limited utility of neutral microsatellites for assigning individuals. Here, we investigated the efficacy of an outlier-based approach to fisheries management by screening >11,000 expressed sequence tags for linked microsatellites and conducting genomic scans for kokanee sampled across seven spawning sites. We identified eight outliers among 52 polymorphic loci that detected ecotype-level divergence, whereas there was no evidence of divergence at neutral loci. Outlier loci exhibited the highest self-assignment accuracy to ecotype (92.1%), substantially outperforming 44 neutral loci (71.8%). Results were robust among-sampling years, with assignment and mixed composition estimates for individuals sampled in 2010 mirroring baseline results. Overall, outlier loci constitute promising alternatives for informing fisheries management involving recently diverged stocks, with potential applications for designating management units across a broad range of taxa., Usage notesOkanagan_Lake_kokanee_microsatellite_dataLength, in base-pairs, of alleles at up to 52 EST-linked and non-EST-linked microsatellite loci in 164 individual kokanee (Oncorhynchus nerka) sampled at seven spawning sites across Okanagan Lake, British Columbia over two sampling years (2007 and 2010). File in GenAlEx format with missing data coded as 0. Data collected with funds from NSERC, Habitat Conservation Trust Fund and Northwest Scientific Association.

Outliers are often present in large datasets of water quality monitoring time series data. A method of combining the sliding window technique with Dixon detection criterion for the automatic detection of outliers in time series data is limited by the empirical determination of sliding window sizes. The scientific determination of the optimal sliding window size is very meaningful research work. This paper presents a new Monte Carlo Search Method (MCSM) based on random sampling to optimize the size of the sliding window, which fully takes advantage of computers and statistics. The MCSM was applied in a case study to automatic monitoring data of water quality factors in order to test its validity and usefulness. The results of comparing the accuracy and efficiency of the MCSM show that the new method in this paper is scientific and effective. The experimental results show that, at different sample sizes, the average accuracy is between 58.70% and 75.75%, and the average computation time increase is between 17.09% and 45.53%. In the era of big data in environmental monitoring, the proposed new methods can meet the required accuracy of outlier detection and improve the efficiency of calculation.

This paper presents a sliding window constrained fault-tolerant filtering method for sampling data in petrochemical instrumentation. The method requires the design of an appropriate sliding window width based on the time series, as well as the expansion of both ends of the series. By utilizing a sliding window constraint function, the method produces a smoothed estimate for the current moment within the window. As the window advances, a series of smoothed estimates of the original sampled data is generated. Subsequently, the original series is subtracted from this smoothed estimate to create a new series that represents the differences between the two. This difference series is then subjected to an additional smoothing estimation process, and the resulting smoothed estimates are employed to compensate for the smoothed estimates of original sampled series. The experimental results indicate that, compared with sliding mean filtering, sliding median filtering, and Savitzky-Golay filtering,..., , , # Sliding window constrained fault-tolerant filtering of compressor vibration data

https://doi.org/10.5061/dryad.pc866t20z

Description of the data and file structure

Data type

Files containing ‘fdata1case1’ in the file represents the case "1" of the location of the outlier in the measured data "1", and so on;

Files containing ‘fwavedata’ in the file name are wave signals with outliers;

Files containing ‘fwave2data’ in the file name are polynomial signals with outliers;

Files containing ‘normaldata’ in the file name are normal measured data;

Files containing ‘normalwavedata’ in the file name are normal wave signals;

Files containing ‘normalwave2data’ in the file name are normal polynomial signals;

Files containing ‘ftffiltered’ in the file name indicate that the data have been processed by sliding-window constrained error-tolerant filtering;

Files containing ‘sgfiltered’ in the file name indicate data after Savitzky-Golay filtering...