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Anomaly Detection Market Size 2024-2028

The anomaly detection market size is forecast to increase by USD 3.71 billion at a CAGR of 13.63% between 2023 and 2028. Anomaly detection is a critical aspect of cybersecurity, particularly in sectors like healthcare where abnormal patient conditions or unusual network activity can have significant consequences. The market for anomaly detection solutions is experiencing significant growth due to several factors. Firstly, the increasing incidence of internal threats and cyber frauds has led organizations to invest in advanced tools for detecting and responding to anomalous behavior. Secondly, the infrastructural requirements for implementing these solutions are becoming more accessible, making them a viable option for businesses of all sizes. Data science and machine learning algorithms play a crucial role in anomaly detection, enabling accurate identification of anomalies and minimizing the risk of incorrect or misleading conclusions.

However, data quality is a significant challenge in this field, as poor quality data can lead to false positives or false negatives, undermining the effectiveness of the solution. Overall, the market for anomaly detection solutions is expected to grow steadily in the coming years, driven by the need for enhanced cybersecurity and the increasing availability of advanced technologies.

What will be the Anomaly Detection Market Size During the Forecast Period?

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Anomaly detection, also known as outlier detection, is a critical data analysis technique used to identify observations or events that deviate significantly from the normal behavior or expected patterns in data. These deviations, referred to as anomalies or outliers, can indicate infrastructure failures, breaking changes, manufacturing defects, equipment malfunctions, or unusual network activity. In various industries, including manufacturing, cybersecurity, healthcare, and data science, anomaly detection plays a crucial role in preventing incorrect or misleading conclusions. Artificial intelligence and machine learning algorithms, such as statistical tests (Grubbs test, Kolmogorov-Smirnov test), decision trees, isolation forest, naive Bayesian, autoencoders, local outlier factor, and k-means clustering, are commonly used for anomaly detection.

Furthermore, these techniques help identify anomalies by analyzing data points and their statistical properties using charts, visualization, and ML models. For instance, in manufacturing, anomaly detection can help identify defective products, while in cybersecurity, it can detect unusual network activity. In healthcare, it can be used to identify abnormal patient conditions. By applying anomaly detection techniques, organizations can proactively address potential issues and mitigate risks, ensuring optimal performance and security.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  Cloud
  On-premise


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  South America



  Middle East and Africa

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period. The market is witnessing a notable shift towards cloud-based solutions due to their numerous advantages over traditional on-premises systems. Cloud-based anomaly detection offers breaking changes such as quicker deployment, enhanced flexibility, and scalability, real-time data visibility, and customization capabilities. These features are provided by service providers with flexible payment models like monthly subscriptions and pay-as-you-go, making cloud-based software a cost-effective and economical choice. Anodot, Ltd, Cisco Systems Inc, IBM Corp, and SAS Institute Inc are some prominent companies offering cloud-based anomaly detection solutions in addition to on-premise alternatives. In the context of security threats, architectural optimization, marketing strategies, finance, fraud detection, manufacturing, and defects, equipment malfunctions, cloud-based anomaly detection is becoming increasingly popular due to its ability to provide real-time insights and swift response to anomalies.

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The cloud segment accounted for USD 1.59 billion in 2018 and showed a gradual increase during the forecast period.

Regional Insights

When it comes to Anomaly Detection Market growth, North America is estimated to contribute 37% to the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast per

Full title: Mining Distance-Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule

Abstract: Defining outliers by their distance to neighboring examples is a popular approach to finding unusual examples in a data set. Recently, much work has been conducted with the goal of finding fast algorithms for this task. We show that a simple nested loop algorithm that in the worst case is quadratic can give near linear time performance when the data is in random order and a simple pruning rule is used. We test our algorithm on real high-dimensional data sets with millions of examples and show that the near linear scaling holds over several orders of magnitude. Our average case analysis suggests that much of the efficiency is because the time to process non-outliers, which are the majority of examples, does not depend on the size of the data set.

QST, a measure of quantitative genetic differentiation among populations, is an index that can suggest local adaptation if QST for a trait is sufficiently larger than the mean FST of neutral genetic markers. A previous method by Whitlock and Guillaume derived a simulation resampling approach to statistically test for a difference between QST and FST, but that method is limited to balanced data sets with offspring related as half-sibs through shared fathers. We extend this approach to (1) allow for a model more suitable for some plant populations or breeding designs in which offspring are related through mothers (assuming independent fathers for each offspring; half-sibs by dam), and (2) by explicitly allowing for unbalanced data sets. The resulting approach is made available through the R package QstFstComp.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of physical illnesses that are linked with obesity and inactivity. Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to:- Asthma (in persons of all ages)- Cancer (in persons of all ages)- Chronic kidney disease (in adults aged 18+)- Coronary heart disease (in persons of all ages)- Diabetes mellitus (in persons aged 17+)- Hypertension (in persons of all ages)- Stroke and transient ischaemic attack (in persons of all ages)This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.For each of the above illnesses, the percentage of each MSOA’s population with that illness was estimated. This was achieved by calculating a weighted average based on:- The percentage of the MSOA area that was covered by each GP practice’s catchment area- Of the GPs that covered part of that MSOA: the percentage of patients registered with each GP that have that illnessThe estimated percentage of each MSOA’s population with each illness was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with each illness, within the relevant age range.For each illness, each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have that illnessB) the NUMBER of people within that MSOA who are estimated to have that illnessAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA predicted to have that illness, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from an illness, and where those people make up a large percentage of the population, indicating there is a real issue with that illness within the population and the investment of resources to address that issue could have the greatest benefits.The scores for each of the 7 illnesses were added together then converted to a relative score between 1 – 0 (1 = worst, 0 = best), to give an overall score for each MSOA: a score close to 1 would indicate that an area has high predicted levels of all obesity/inactivity-related illnesses, and these are areas where the local population could benefit the most from interventions to address those illnesses. A score close to 0 would indicate very low predicted levels of obesity/inactivity-related illnesses and therefore interventions might not be required.LIMITATIONS1. GPs do not have catchments that are mutually exclusive from each other: they overlap, with some geographic areas being covered by 30+ practices. This dataset should be viewed in combination with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset to identify where there are areas that are covered by multiple GP practices but at least one of those GP practices did not provide data. Results of the analysis in these areas should be interpreted with caution, particularly if the levels of obesity/inactivity-related illnesses appear to be significantly lower than the immediate surrounding areas.2. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).3. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.4. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of obesity/inactivity-related illnesses, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of these illnesses. TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:- Health and wellbeing statistics (GP-level, England): Missing data and potential outliersDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Global Navigation Satellite System (GNSS) Station unavco/gnss/Nucleus/QCY2/1449/L0/00:00:15 Name: QCY2_BARD_CN2006 Processing Level: L0 measurement_technique: gnss variable_measured: position creator:UNAVCO data_start_time:2006-05-17T05:48:45 data_stop_time:2018-03-22T05:59:45 GPS/GNSS instrumentation records broadcast signals from the GPS and other satellite constellation, and these raw data are converted into standard daily RINEX files suitable for processing. GPS/GNSS data are recorded at 15-s or 30-s intervals. Several hundred stations of the PBO network also supply downloaded or streamed 1-s data for archiving and distribution. In addition highrate data of 1 Hz or 5 Hz may be Custom Data Requested in association with an event such as a significant earthquake. For data of all rates UNAVCO translates to RINEX and quality checks the data using teqc. GAGE Analysis Centers process data for all 1100 sites in the PBO GPS/GNSS network and for other sites, including most of the sites in COCONet in the Caribbean region and an additional 500 sites distributed across North America, most of which are operated by other institutions. The final, processed products are SINEX solutions, position ti Web Service Link ['The hydrologic models are surface-loading displacement time series calculated at GAGE-processed sites from hydrological data. Soil moisture, snow-water equivalent from snowpack, and water stored in vegetation exert a load on the Earth's surface that is modeled to obtain displacements at GPS/GNSS sites. Outputs GPS crustal motion velocity field estimates. '] Web Service Link [ 'Results from daily GPS station position solutions are combined to generate long-term velocity estimate solutions of stations in IGS08 and NAM08 (North America fixed) reference frames. Station offsets due to earthquakes and equipment changes are estimated and low-quality outliers due to snow, for example, are removed from the velocity estimate solutions ']

AbstractQST, a measure of quantitative genetic differentiation among populations, is an index that can suggest local adaptation if QST for a trait is sufficiently larger than the mean FST of neutral genetic markers. A previous method by Whitlock and Guillaume derived a simulation resampling approach to statistically test for a difference between QST and FST, but that method is limited to balanced data sets with offspring related as half-sibs through shared fathers. We extend this approach to (1) allow for a model more suitable for some plant populations or breeding designs in which offspring are related through mothers (assuming independent fathers for each offspring; half-sibs by dam), and (2) by explicitly allowing for unbalanced data sets. The resulting approach is made available through the R package QstFstComp. Usage notesSourceCode_DamModelSource code used when doing type I error testing of balanced or unbalanced half-sib dam modelDamModel_WorkingCopy.RSireModel_WorkingCopySource code used when doing type I error testing of unbalanced half-sib sire modelTypeI_ErrorTest_DamBalancedR code to run the error testing of the balanced half-sib dam model over 1000 replicate datasets.TypeI_ErrorTest_DamUnbalancedR code to run the error testing of the unbalanced half-sib dam model over 1000 replicate datasets.TypeI_ErrorTest_SireUnbalancedR code to run the error testing of the unbalanced half-sib sire model over 1000 replicate datasets.NemoReplicatesZipped file containing the 1000 simulated replicate datasets from Nemo used for type I error testing.

Global Navigation Satellite System (GNSS) Station unavco/gnss/Cascadia/P231/7898/L1/00:00:01 Name: HopkinsStnCN2006_1HZ Processing Level: L1 measurement_technique: gnss variable_measured: position creator:UNAVCO data_start_time:2016-06-25T07:12:23 data_stop_time:2018-03-22T06:00:00 GPS/GNSS instrumentation records broadcast signals from the GPS and other satellite constellation, and these raw data are converted into standard daily RINEX files suitable for processing. GPS/GNSS data are recorded at 15-s or 30-s intervals. Several hundred stations of the PBO network also supply downloaded or streamed 1-s data for archiving and distribution. In addition highrate data of 1 Hz or 5 Hz may be Custom Data Requested in association with an event such as a significant earthquake. For data of all rates UNAVCO translates to RINEX and quality checks the data using teqc. GAGE Analysis Centers process data for all 1100 sites in the PBO GPS/GNSS network and for other sites, including most of the sites in COCONet in the Caribbean region and an additional 500 sites distributed across North America, most of which are operated by other institutions. The final, processed products are SINEX solutions, position ti Web Service Link ['The hydrologic models are surface-loading displacement time series calculated at GAGE-processed sites from hydrological data. Soil moisture, snow-water equivalent from snowpack, and water stored in vegetation exert a load on the Earth's surface that is modeled to obtain displacements at GPS/GNSS sites. Outputs GPS crustal motion velocity field estimates. '] Web Service Link [ 'Results from daily GPS station position solutions are combined to generate long-term velocity estimate solutions of stations in IGS08 and NAM08 (North America fixed) reference frames. Station offsets due to earthquakes and equipment changes are estimated and low-quality outliers due to snow, for example, are removed from the velocity estimate solutions ']

Abstract

The high-frequency phone survey of refugees monitors the economic and social impact of and responses to the COVID-19 pandemic on refugees and nationals, by calling a sample of households every four weeks. The main objective is to inform timely and adequate policy and program responses. Since the outbreak of the COVID-19 pandemic in Ethiopia, two rounds of data collection of refugees were completed between September and November 2020. The first round of the joint national and refugee HFPS was implemented between the 24 September and 17 October 2020 and the second round between 20 October and 20 November 2020.

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was drawn using a simple random sample without replacement. Expecting a high non-response rate based on experience from the HFPS-HH, we drew a stratified sample of 3,300 refugee households for the first round. More details on sampling methodology are provided in the Survey Methodology Document available for download as Related Materials.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The Ethiopia COVID-19 High Frequency Phone Survey of Refugee questionnaire consists of the following sections:

• Interview Information
• Household Roster
• Camp Information
• Knowledge Regarding the Spread of COVID-19
• Behaviour and Social Distancing - Access to Basic Services
• Employment
• Income Loss
• Coping/Shocks
• Social Relations
• Food Security
• Aid and Support/ Social Safety Nets.

A more detailed description of the questionnaire is provided in Table 1 of the Survey Methodology Document that is provided as Related Materials. Round 1 and 2 questionnaires available for download.

Cleaning operations

DATA CLEANING At the end of data collection, the raw dataset was cleaned by the Research team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes. Data cleaning carried out is detailed below.

Variable naming and labeling: • Variable names were changed to reflect the lowercase question name in the paper survey copy, and a word or two related to the question. • Variables were labeled with longer descriptions of their contents and the full question text was stored in Notes for each variable. • “Other, specify” variables were named similarly to their related question, with “_other” appended to the name. • Value labels were assigned where relevant, with options shown in English for all variables, unless preloaded from the roster in Amharic.

Variable formatting: • Variables were formatted as their object type (string, integer, decimal, time, date, or datetime). • Multi-select variables were saved both in space-separated single-variables and as multiple binary variables showing the yes/no value of each possible response. • Time and date variables were stored as POSIX timestamp values and formatted to show Gregorian dates. • Location information was left in separate ID and Name variables, following the format of the incoming roster. IDs were formatted to include only the variable level digits, and not the higher-level prefixes (2-3 digits only.)
• Only consented surveys were kept in the dataset, and all personal information and internal survey variables were dropped from the clean dataset. • Roster data is separated from the main data set and kept in long-form but can be merged on the key variable (key can also be used to merge with the raw data). • The variables were arranged in the same order as the paper instrument, with observations arranged according to their submission time.

Backcheck data review: Results of the backcheck survey are compared against the originally captured survey results using the bcstats command in Stata. This function delivers a comparison of variables and identifies any discrepancies. Any discrepancies identified are then examined individually to determine if they are within reason.

Data appraisal

The following data quality checks were completed: • Daily SurveyCTO monitoring: This included outlier checks, skipped questions, a review of “Other, specify”, other text responses, and enumerator comments. Enumerator comments were used to suggest new response options or to highlight situations where existing options should be used instead. Monitoring also included a review of variable relationship logic checks and checks of the logic of answers. Finally, outliers in phone variables such as survey duration or the percentage of time audio was at a conversational level were monitored. A survey duration of close to 15 minutes and a conversation-level audio percentage of around 40% was considered normal. • Dashboard review: This included monitoring individual enumerator performance, such as the number of calls logged, duration of calls, percentage of calls responded to and percentage of non-consents. Non-consent reason rates and attempts per household were monitored as well. Duration analysis using R was used to monitor each module's duration and estimate the time required for subsequent rounds. The dashboard was also used to track overall survey completion and preview the results of key questions. • Daily Data Team reporting: The Field Supervisors and the Data Manager reported daily feedback on call progress, enumerator feedback on the survey, and any suggestions to improve the instrument, such as adding options to multiple choice questions or adjusting translations. • Audio audits: Audio recordings were captured during the consent portion of the interview for all completed interviews, for the enumerators' side of the conversation only. The recordings were reviewed for any surveys flagged by enumerators as having data quality concerns and for an additional random sample of 2% of respondents. A range of lengths were selected to observe edge cases. Most consent readings took around one minute, with some longer recordings due to questions on the survey or holding for the respondent. All reviewed audio recordings were completed satisfactorily. • Back-check survey: Field Supervisors made back-check calls to a random sample of 5% of the households that completed a survey in Round 1. Field Supervisors called these households and administered a short survey, including (i) identifying the same respondent; (ii) determining the respondent's position within the household; (iii) confirming that a member of the the data collection team had completed the interview; and (iv) a few questions from the original survey.

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.

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...

Global Navigation Satellite System (GNSS) Station unr/gnss/G206/4652/L2/24:00:00 Name: G206 Processing Level: L2 measurement_technique: gnss variable_measured: position creator:GSI, JAPAN data_start_time:2003-06-18T00:00:00 data_stop_time:2018-03-16T00:00:00 GPS/GNSS instrumentation records broadcast signals from the GPS and other satellite constellation, and these raw data are converted into standard daily RINEX files suitable for processing. GPS/GNSS data are recorded at 15-s or 30-s intervals. Several hundred stations of the PBO network also supply downloaded or streamed 1-s data for archiving and distribution. In addition highrate data of 1 Hz or 5 Hz may be Custom Data Requested in association with an event such as a significant earthquake. For data of all rates UNAVCO translates to RINEX and quality checks the data using teqc. GAGE Analysis Centers process data for all 1100 sites in the PBO GPS/GNSS network and for other sites, including most of the sites in COCONet in the Caribbean region and an additional 500 sites distributed across North America, most of which are operated by other institutions. The final, processed products are SINEX solutions, position ti Web Service Link ['The hydrologic models are surface-loading displacement time series calculated at GAGE-processed sites from hydrological data. Soil moisture, snow-water equivalent from snowpack, and water stored in vegetation exert a load on the Earth's surface that is modeled to obtain displacements at GPS/GNSS sites. Outputs GPS crustal motion velocity field estimates. '] Web Service Link [ 'Results from daily GPS station position solutions are combined to generate long-term velocity estimate solutions of stations in IGS08 and NAM08 (North America fixed) reference frames. Station offsets due to earthquakes and equipment changes are estimated and low-quality outliers due to snow, for example, are removed from the velocity estimate solutions ']

Global Navigation Satellite System (GNSS) Station unavco/gnss/PBO-Nucleus High Rate/QCY2/5434/L0/00:00:01 Name: QCY2_BARD_CN2006_1HZ Processing Level: L0 measurement_technique: gnss variable_measured: position creator:UNAVCO data_start_time:2011-03-07T07:00:00 data_stop_time:2015-07-20T05:59:59 GPS/GNSS instrumentation records broadcast signals from the GPS and other satellite constellation, and these raw data are converted into standard daily RINEX files suitable for processing. GPS/GNSS data are recorded at 15-s or 30-s intervals. Several hundred stations of the PBO network also supply downloaded or streamed 1-s data for archiving and distribution. In addition highrate data of 1 Hz or 5 Hz may be Custom Data Requested in association with an event such as a significant earthquake. For data of all rates UNAVCO translates to RINEX and quality checks the data using teqc. GAGE Analysis Centers process data for all 1100 sites in the PBO GPS/GNSS network and for other sites, including most of the sites in COCONet in the Caribbean region and an additional 500 sites distributed across North America, most of which are operated by other institutions. The final, processed products are SINEX solutions, position ti Web Service Link ['The hydrologic models are surface-loading displacement time series calculated at GAGE-processed sites from hydrological data. Soil moisture, snow-water equivalent from snowpack, and water stored in vegetation exert a load on the Earth's surface that is modeled to obtain displacements at GPS/GNSS sites. Outputs GPS crustal motion velocity field estimates. '] Web Service Link [ 'Results from daily GPS station position solutions are combined to generate long-term velocity estimate solutions of stations in IGS08 and NAM08 (North America fixed) reference frames. Station offsets due to earthquakes and equipment changes are estimated and low-quality outliers due to snow, for example, are removed from the velocity estimate solutions ']

Global Navigation Satellite System (GNSS) Station unavco/gnss/Indonesia/NABI/474/L1/00:00:30 Name: Nabire -Indonesia Processing Level: L1 measurement_technique: gnss variable_measured: position creator:UNAVCO data_start_time:2002-03-01T20:29:30 data_stop_time:2003-11-01T07:00:30 GPS/GNSS instrumentation records broadcast signals from the GPS and other satellite constellation, and these raw data are converted into standard daily RINEX files suitable for processing. GPS/GNSS data are recorded at 15-s or 30-s intervals. Several hundred stations of the PBO network also supply downloaded or streamed 1-s data for archiving and distribution. In addition highrate data of 1 Hz or 5 Hz may be Custom Data Requested in association with an event such as a significant earthquake. For data of all rates UNAVCO translates to RINEX and quality checks the data using teqc. GAGE Analysis Centers process data for all 1100 sites in the PBO GPS/GNSS network and for other sites, including most of the sites in COCONet in the Caribbean region and an additional 500 sites distributed across North America, most of which are operated by other institutions. The final, processed products are SINEX solutions, position ti Web Service Link ['The hydrologic models are surface-loading displacement time series calculated at GAGE-processed sites from hydrological data. Soil moisture, snow-water equivalent from snowpack, and water stored in vegetation exert a load on the Earth's surface that is modeled to obtain displacements at GPS/GNSS sites. Outputs GPS crustal motion velocity field estimates. '] Web Service Link [ 'Results from daily GPS station position solutions are combined to generate long-term velocity estimate solutions of stations in IGS08 and NAM08 (North America fixed) reference frames. Station offsets due to earthquakes and equipment changes are estimated and low-quality outliers due to snow, for example, are removed from the velocity estimate solutions ']

Global Navigation Satellite System (GNSS) Station unavco/gnss/PBO-Nucleus High Rate/QCY2/2565/L0/00:00:00.2 Name: QCY2_BARD_CN2006_5HZ Processing Level: L0 measurement_technique: gnss variable_measured: position creator:UNAVCO data_start_time:2006-11-12T07:00:00 data_stop_time:2014-08-25T05:59:59 GPS/GNSS instrumentation records broadcast signals from the GPS and other satellite constellation, and these raw data are converted into standard daily RINEX files suitable for processing. GPS/GNSS data are recorded at 15-s or 30-s intervals. Several hundred stations of the PBO network also supply downloaded or streamed 1-s data for archiving and distribution. In addition highrate data of 1 Hz or 5 Hz may be Custom Data Requested in association with an event such as a significant earthquake. For data of all rates UNAVCO translates to RINEX and quality checks the data using teqc. GAGE Analysis Centers process data for all 1100 sites in the PBO GPS/GNSS network and for other sites, including most of the sites in COCONet in the Caribbean region and an additional 500 sites distributed across North America, most of which are operated by other institutions. The final, processed products are SINEX solutions, position ti Web Service Link ['The hydrologic models are surface-loading displacement time series calculated at GAGE-processed sites from hydrological data. Soil moisture, snow-water equivalent from snowpack, and water stored in vegetation exert a load on the Earth's surface that is modeled to obtain displacements at GPS/GNSS sites. Outputs GPS crustal motion velocity field estimates. '] Web Service Link [ 'Results from daily GPS station position solutions are combined to generate long-term velocity estimate solutions of stations in IGS08 and NAM08 (North America fixed) reference frames. Station offsets due to earthquakes and equipment changes are estimated and low-quality outliers due to snow, for example, are removed from the velocity estimate solutions ']

Global Navigation Satellite System (GNSS) Station unavco/gnss/Nucleus/QCYN/570/L0/00:00:30 Name: QCYN_BARD_CN2003 Processing Level: L0 measurement_technique: gnss variable_measured: position creator:UNAVCO data_start_time:2003-01-24T05:27:00 data_stop_time:2006-06-12T06:00:00 GPS/GNSS instrumentation records broadcast signals from the GPS and other satellite constellation, and these raw data are converted into standard daily RINEX files suitable for processing. GPS/GNSS data are recorded at 15-s or 30-s intervals. Several hundred stations of the PBO network also supply downloaded or streamed 1-s data for archiving and distribution. In addition highrate data of 1 Hz or 5 Hz may be Custom Data Requested in association with an event such as a significant earthquake. For data of all rates UNAVCO translates to RINEX and quality checks the data using teqc. GAGE Analysis Centers process data for all 1100 sites in the PBO GPS/GNSS network and for other sites, including most of the sites in COCONet in the Caribbean region and an additional 500 sites distributed across North America, most of which are operated by other institutions. The final, processed products are SINEX solutions, position ti Web Service Link ['The hydrologic models are surface-loading displacement time series calculated at GAGE-processed sites from hydrological data. Soil moisture, snow-water equivalent from snowpack, and water stored in vegetation exert a load on the Earth's surface that is modeled to obtain displacements at GPS/GNSS sites. Outputs GPS crustal motion velocity field estimates. '] Web Service Link [ 'Results from daily GPS station position solutions are combined to generate long-term velocity estimate solutions of stations in IGS08 and NAM08 (North America fixed) reference frames. Station offsets due to earthquakes and equipment changes are estimated and low-quality outliers due to snow, for example, are removed from the velocity estimate solutions ']

Global Navigation Satellite System (GNSS) Station unavco/gnss/PBO-Nucleus High Rate/QCY2/5434/L1/00:00:01 Name: QCY2_BARD_CN2006_1HZ Processing Level: L1 measurement_technique: gnss variable_measured: position creator:UNAVCO data_start_time:2011-03-07T07:00:00 data_stop_time:2015-07-20T05:59:59 GPS/GNSS instrumentation records broadcast signals from the GPS and other satellite constellation, and these raw data are converted into standard daily RINEX files suitable for processing. GPS/GNSS data are recorded at 15-s or 30-s intervals. Several hundred stations of the PBO network also supply downloaded or streamed 1-s data for archiving and distribution. In addition highrate data of 1 Hz or 5 Hz may be Custom Data Requested in association with an event such as a significant earthquake. For data of all rates UNAVCO translates to RINEX and quality checks the data using teqc. GAGE Analysis Centers process data for all 1100 sites in the PBO GPS/GNSS network and for other sites, including most of the sites in COCONet in the Caribbean region and an additional 500 sites distributed across North America, most of which are operated by other institutions. The final, processed products are SINEX solutions, position ti Web Service Link ['The hydrologic models are surface-loading displacement time series calculated at GAGE-processed sites from hydrological data. Soil moisture, snow-water equivalent from snowpack, and water stored in vegetation exert a load on the Earth's surface that is modeled to obtain displacements at GPS/GNSS sites. Outputs GPS crustal motion velocity field estimates. '] Web Service Link [ 'Results from daily GPS station position solutions are combined to generate long-term velocity estimate solutions of stations in IGS08 and NAM08 (North America fixed) reference frames. Station offsets due to earthquakes and equipment changes are estimated and low-quality outliers due to snow, for example, are removed from the velocity estimate solutions ']

Global Navigation Satellite System (GNSS) Station unavco/gnss/PBO-Nucleus High Rate/QCY2/2565/L1/00:00:00.2 Name: QCY2_BARD_CN2006_5HZ Processing Level: L1 measurement_technique: gnss variable_measured: position creator:UNAVCO data_start_time:2006-11-12T07:00:00 data_stop_time:2014-08-25T05:59:59 GPS/GNSS instrumentation records broadcast signals from the GPS and other satellite constellation, and these raw data are converted into standard daily RINEX files suitable for processing. GPS/GNSS data are recorded at 15-s or 30-s intervals. Several hundred stations of the PBO network also supply downloaded or streamed 1-s data for archiving and distribution. In addition highrate data of 1 Hz or 5 Hz may be Custom Data Requested in association with an event such as a significant earthquake. For data of all rates UNAVCO translates to RINEX and quality checks the data using teqc. GAGE Analysis Centers process data for all 1100 sites in the PBO GPS/GNSS network and for other sites, including most of the sites in COCONet in the Caribbean region and an additional 500 sites distributed across North America, most of which are operated by other institutions. The final, processed products are SINEX solutions, position ti Web Service Link ['The hydrologic models are surface-loading displacement time series calculated at GAGE-processed sites from hydrological data. Soil moisture, snow-water equivalent from snowpack, and water stored in vegetation exert a load on the Earth's surface that is modeled to obtain displacements at GPS/GNSS sites. Outputs GPS crustal motion velocity field estimates. '] Web Service Link [ 'Results from daily GPS station position solutions are combined to generate long-term velocity estimate solutions of stations in IGS08 and NAM08 (North America fixed) reference frames. Station offsets due to earthquakes and equipment changes are estimated and low-quality outliers due to snow, for example, are removed from the velocity estimate solutions ']

Global Navigation Satellite System (GNSS) Station unavco/gnss/Rio Grande Rift/RG18/3005/L1/00:00:30 Name: RG18TwLakeCO2007 Processing Level: L1 measurement_technique: gnss variable_measured: position creator:UNAVCO data_start_time:2007-06-01T00:37:00 data_stop_time:2016-05-05T22:13:30 GPS/GNSS instrumentation records broadcast signals from the GPS and other satellite constellation, and these raw data are converted into standard daily RINEX files suitable for processing. GPS/GNSS data are recorded at 15-s or 30-s intervals. Several hundred stations of the PBO network also supply downloaded or streamed 1-s data for archiving and distribution. In addition highrate data of 1 Hz or 5 Hz may be Custom Data Requested in association with an event such as a significant earthquake. For data of all rates UNAVCO translates to RINEX and quality checks the data using teqc. GAGE Analysis Centers process data for all 1100 sites in the PBO GPS/GNSS network and for other sites, including most of the sites in COCONet in the Caribbean region and an additional 500 sites distributed across North America, most of which are operated by other institutions. The final, processed products are SINEX solutions, position ti Web Service Link ['The hydrologic models are surface-loading displacement time series calculated at GAGE-processed sites from hydrological data. Soil moisture, snow-water equivalent from snowpack, and water stored in vegetation exert a load on the Earth's surface that is modeled to obtain displacements at GPS/GNSS sites. Outputs GPS crustal motion velocity field estimates. '] Web Service Link [ 'Results from daily GPS station position solutions are combined to generate long-term velocity estimate solutions of stations in IGS08 and NAM08 (North America fixed) reference frames. Station offsets due to earthquakes and equipment changes are estimated and low-quality outliers due to snow, for example, are removed from the velocity estimate solutions ']

Global Navigation Satellite System (GNSS) Station unavco/gnss/Hawaii L1/HV02/276/L1/00:00:10 Name: Halemaumau Processing Level: L1 measurement_technique: gnss variable_measured: position creator:UNAVCO data_start_time:1999-07-04T03:06:30 data_stop_time:2004-01-14T20:19:50 GPS/GNSS instrumentation records broadcast signals from the GPS and other satellite constellation, and these raw data are converted into standard daily RINEX files suitable for processing. GPS/GNSS data are recorded at 15-s or 30-s intervals. Several hundred stations of the PBO network also supply downloaded or streamed 1-s data for archiving and distribution. In addition highrate data of 1 Hz or 5 Hz may be Custom Data Requested in association with an event such as a significant earthquake. For data of all rates UNAVCO translates to RINEX and quality checks the data using teqc. GAGE Analysis Centers process data for all 1100 sites in the PBO GPS/GNSS network and for other sites, including most of the sites in COCONet in the Caribbean region and an additional 500 sites distributed across North America, most of which are operated by other institutions. The final, processed products are SINEX solutions, position ti Web Service Link ['The hydrologic models are surface-loading displacement time series calculated at GAGE-processed sites from hydrological data. Soil moisture, snow-water equivalent from snowpack, and water stored in vegetation exert a load on the Earth's surface that is modeled to obtain displacements at GPS/GNSS sites. Outputs GPS crustal motion velocity field estimates. '] Web Service Link [ 'Results from daily GPS station position solutions are combined to generate long-term velocity estimate solutions of stations in IGS08 and NAM08 (North America fixed) reference frames. Station offsets due to earthquakes and equipment changes are estimated and low-quality outliers due to snow, for example, are removed from the velocity estimate solutions ']

Global Navigation Satellite System (GNSS) Station unavco/gnss/Salmon Falls Creek/STOE/8304/L2/00:00:15 Name: South Toe Slope Processing Level: L2 measurement_technique: gnss variable_measured: position creator:UNAVCO data_start_time:2017-02-06T04:33:15 data_stop_time:2018-01-24T03:53:45 GPS/GNSS instrumentation records broadcast signals from the GPS and other satellite constellation, and these raw data are converted into standard daily RINEX files suitable for processing. GPS/GNSS data are recorded at 15-s or 30-s intervals. Several hundred stations of the PBO network also supply downloaded or streamed 1-s data for archiving and distribution. In addition highrate data of 1 Hz or 5 Hz may be Custom Data Requested in association with an event such as a significant earthquake. For data of all rates UNAVCO translates to RINEX and quality checks the data using teqc. GAGE Analysis Centers process data for all 1100 sites in the PBO GPS/GNSS network and for other sites, including most of the sites in COCONet in the Caribbean region and an additional 500 sites distributed across North America, most of which are operated by other institutions. The final, processed products are SINEX solutions, position ti Web Service Link ['The hydrologic models are surface-loading displacement time series calculated at GAGE-processed sites from hydrological data. Soil moisture, snow-water equivalent from snowpack, and water stored in vegetation exert a load on the Earth's surface that is modeled to obtain displacements at GPS/GNSS sites. Outputs GPS crustal motion velocity field estimates. '] Web Service Link [ 'Results from daily GPS station position solutions are combined to generate long-term velocity estimate solutions of stations in IGS08 and NAM08 (North America fixed) reference frames. Station offsets due to earthquakes and equipment changes are estimated and low-quality outliers due to snow, for example, are removed from the velocity estimate solutions ']