File List adaptiveMH.r (md5: 1c7f3697e28dca0aceda63360930e29f) adaptiveMHfuns.r (md5: cabc33a60ab779b954d853816c9e3cce) PF.r (md5: eff6f6611833c86c1d1a8e8135af7e04)

Description
  adaptiveMH.r – Contains a script for fitting a random-walk model with drift for Kangaroo population dynamics on the log-scale using particle filtering Metropolis Hastings with an initial adaptive phase.
  adaptiveMHfuns.r – Contains functions that are used for estimating and handling the normal mixture proposals.
  PF.r – Contains functions that perform the particle filtering and define the model.

This are the files needed for running the experiments of "Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation".

• listening_events.tsv.bz2 : Dataset excerpt from LFM-2b, before filtering (see submission for details)

SummaryGenotype Query Tools (GQT) were developed to discover disease-causing variations from billions of genotypes and millions of genomes, processes data at substantially higher speed over other existing methods. While GQT has been available to a wide audience as command-line software, the difficulty of constructing queries among non-IT or non-bioinformatics researchers has limited its applicability. To overcome this limitation, we developed webGQT, an easy-to-use tool with a graphical user interface. With pre-built queries across three modules, webGQT allows for pedigree analysis, case-control studies, and population frequency studies. As a package, webGQT allows researchers with less or no applied bioinformatics/IT experience to mine potential disease-causing variants from billions.ResultswebGQT offers a flexible and easy-to-use interface for model-based candidate variant filtering for Mendelian diseases from thousands to millions of genomes at a reduced computation time. Additionally, webGQT provides adjustable parameters to reduce false positives and rescue missing genotypes across all modules. Using a case study, we demonstrate the applicability of webGQT to query non-human genomes. In addition, we demonstrate the scalability of webGQT on large data sets by implementing complex population-specific queries on the 1000 Genomes Project Phase 3 data set, which includes 8.4 billion variants from 2504 individuals across 26 different populations. Furthermore, webGQT supports filtering single-nucleotide variants, short insertions/deletions, copy number or any other variant genotypes supported by the VCF specification. Our results show that webGQT can be used as an online web service, or deployed on personal computers or local servers within research groups.AvailabilitywebGQT is made available to the users in three forms: 1) as a webserver available at https://vm1138.kaj.pouta.csc.fi/webgqt/, 2) as an R package to install on personal computers, and 3) as part of the same R package to configure on the user's own servers. The application is available for installation at https://github.com/arumds/webgqt.

--- Cleaning Summary --- Dataset : marcuscedricridia/jenna-filtered-polarity Human column used : prompt

Assistant column used : response

Initial size : 7034 After basic cleaning : 7034 After exact deduplication : 6031 After length filtering : 214 After language filtering : 204 After alignment filtering : 204 After boilerplate removal : 204 After near-duplicate… See the full description on the dataset page: https://huggingface.co/datasets/marcuscedricridia/jenna-filtered-polarity-deepclean.

marcuscedricridia/amoralqa-filtered-polarity

  Overview

This dataset is a filtered version of TheDrummer/AmoralQA-v2, created to isolate the most amoral entries based on sentiment analysis.

  Filtering Criteria

Entries were ranked based on their negative sentiment.
Only the most extreme cases were kept.
The filtering process used automated sentiment analysis to determine inclusion.

  Source & Citation

Original Dataset: TheDrummer/AmoralQA-v2… See the full description on the dataset page: https://huggingface.co/datasets/marcuscedricridia/amoralqa-filtered-polarity.

Dataset and R code used for the manuscript:

Von Eggers, J. M., Wisnoski, N. I., Calder, J. W., Capo, E., Groff, D. V., Krist, A. C., & Shuman, B. (2024). Environmental filtering governs consistent vertical zonation in sedimentary microbial communities across disconnected mountain lakes. Environmental Microbiology, 26(3), e16607.

This dataset and code are also available on GitHub (https://github.com/jvoneggers/WYLakeSedMicrobes).

Output files from the No 3. Filtering Workflow page of the SWELTR high- temp study. ASV filtering for 16S rRNA & ITS using a) Arbitrary filtering, b) PERFect (PERmutation Filtering test for microbiome data), and c) PIME (Prevalence Interval for Microbiome Evaluation) Workflow objects:

filtering_wf.rdata: contains all variables and phyloseq objects from 16s rRNA and ITS ASV filtering. To see the Objects , in R run load("_filtering_wf.rdata", verbose=TRUE)_

Additional files:

For convenience, we also include individual phyloseq objects for each filtered data set.

**_Arbitrary_ :
**

****ssu18_ps_filt.rds:**** phyloseq object for Arbitrary filtered 16S rRNA ASVs.****
its18_ps_filt.rds:**** phyloseq object for Arbitrary filtered ITS ASVs.****

PERfect :

******ssu18_ps_perfect.rds** : ****phyloseq object for PERfect filtered 16S rRNA ASVs.****
its18_ps_perfect.rds : ****phyloseq object for PERfect filtered ITS ASVs.****

****_PIME_ : ** **

ssu18_ps_pime.rds : phyloseq object for PIME filtered 16S rRNA ASVs.
its18_ps_pime.rds : phyloseq object for PIME filtered ITS ASVs.

Source code for the workflow can be found here:

https://github.com/sweltr/high-temp/blob/master/filtering.Rmd

Aqueous environmental DNA (eDNA) is an emerging efficient non-invasive tool for species inventory studies. To maximize performance of downstream quantitative PCR (qPCR) and next-generation sequencing (NGS) applications, quality and quantity of the starting material is crucial, calling for optimized capture, storage and extraction techniques of eDNA. Previous comparative studies for eDNA capture/storage have tested precipitation and 'open' filters. However, practical 'enclosed' filters which reduce unnecessary handling have not been included. Here, we fill this gap by comparing a filter capsule (Sterivex-GP polyethersulfone, pore size 0·22 μm, hereafter called SX) with commonly used methods. Our experimental set-up, covering altogether 41 treatments combining capture by precipitation or filtration with different preservation techniques and storage times, sampled one single lake (and a fish-free control pond). We selected documented capture methods that have successfully targeted a wide range of fauna. The eDNA was extracted using an optimized protocol modified from the DNeasy® Blood & Tissue kit (Qiagen). We measured total eDNA concentrations and Cq-values (cycles used for DNA quantification by qPCR) to target specific mtDNA cytochrome b (cyt b) sequences in two local keystone fish species. SX yielded higher amounts of total eDNA along with lower Cq-values than polycarbonate track-etched filters (PCTE), glass fibre filters (GF) or ethanol precipitation (EP). SX also generated lower Cq-values than cellulose nitrate filters (CN) for one of the target species. DNA integrity of SX samples did not decrease significantly after 2 weeks of storage in contrast to GF and PCTE. Adding preservative before storage improved SX results. In conclusion, we recommend SX filters (originally designed for filtering micro-organisms) as an efficient capture method for sampling macrobial eDNA. Ethanol or Longmire's buffer preservation of SX immediately after filtration is recommended. Preserved SX capsules may be stored at room temperature for at least 2 weeks without significant degradation. Reduced handling and less exposure to outside stress compared with other filters may contribute to better eDNA results. SX capsules are easily transported and enable eDNA sampling in remote and harsh field conditions as samples can be filtered/preserved on site.

This dataset contains information about all the features extracted from the raw data files, the formulas that were assigned to some of these features, and the candidate compounds that correspond to those formulas. Data sources, bioactivity, exposure estimates, functional uses, and predicted and observed retention times are available for all candidate compounds. This dataset is associated with the following publication: Newton, S., R. McMahen, J. Sobus, K. Mansouri, A. Williams, A. McEachran, and M. Strynar. Suspect Screening and Non-Targeted Analysis of Drinking Water Using Point-Of-Use Filters. ENVIRONMENTAL POLLUTION. Elsevier Science Ltd, New York, NY, USA, 234: 297-306, (2018).

High Precision Filter Market Outlook

The global market size for high precision filters is projected to grow substantially from USD 5.5 billion in 2023 to USD 8.9 billion by 2032, exhibiting a Compound Annual Growth Rate (CAGR) of 5.5%. This growth is being driven by the increasing demand for high-performance filtration systems across various industries such as automotive, healthcare, and aerospace.

One of the primary growth factors for the high precision filter market is the rising awareness regarding environmental pollution and the stringent regulations imposed by governments worldwide to curb industrial emissions. Companies are increasingly investing in high precision filtration systems to meet environmental standards and ensure compliance. Additionally, advancements in filtration technologies are enabling more efficient and cost-effective solutions, which is attracting a broader customer base and spurring market growth.

Another significant growth driver is the burgeoning demand from the healthcare and automotive sectors. In healthcare, high precision filters are critical for maintaining sterile environments and ensuring the purity of pharmaceuticals. The automotive industry also relies heavily on advanced filtration systems to improve the performance and longevity of vehicles. The increasing adoption of electric and hybrid vehicles, which require specialized filtration systems, is further propelling the market.

The industrial sector is also contributing to market growth. Industries such as manufacturing, oil and gas, and chemicals require high precision filters to maintain the quality of their products and protect machinery from contaminants. The ongoing trend of industrial automation and the Internet of Things (IoT) is prompting industries to adopt more sophisticated filtration systems for better process control and efficiency.

Regionally, the Asia Pacific dominates the global high precision filter market due to rapid industrialization, particularly in countries like China and India. North America and Europe are also significant markets, driven by technological advancements and stringent environmental regulations. The Middle East & Africa and Latin America are emerging markets with substantial growth potential due to increasing industrial activities and infrastructural developments.

Type Analysis

The high precision filter market can be segmented by type into mechanical filters, electronic filters, and fluid filters. Mechanical filters, which include air and particulate filters, are the most commonly used and have a wide range of applications across various industries. These filters rely on physical barriers to remove contaminants and are highly effective in environments where particulate matter is a significant concern.

Electronic filters are gaining traction, especially in the electronics and healthcare sectors. These filters use electrical fields to remove pollutants and are known for their high efficiency and precision. They are particularly useful in applications requiring ultra-clean environments, such as semiconductor manufacturing and medical laboratories. The growing demand for high-performance electronic devices is driving the adoption of electronic filters.

Fluid filters, including hydraulic and lubrication filters, are essential in industries that rely on fluid power systems. These filters ensure the purity of fluids used in machinery, thus enhancing performance and reducing wear and tear. The automotive and aerospace sectors are significant users of fluid filters, and the increasing focus on improving vehicle and aircraft efficiency is boosting the demand for these filters.

Each type of filter has its unique advantages and applications. Companies are increasingly offering customized filtration solutions to meet the specific needs of different industries. The ongoing research and development activities aimed at improving filtration technologies are expected to further enhance the performance and efficiency of these filters, driving market growth.

Report Scope

The global Automatic Backwashing Filters market is forecasted to grow at a noteworthy CAGR of 6.74% between 2025 and 2033. By 2033, market size is expected to surge to USD 7.09 Billion, a substantial rise from the USD 3.94 Billion recorded in 2024.

The Global Automatic Backwashing Filters market size to cross USD 7.09 Billion by 2033. [https://edison.valuemarketresearch.com//uploads/report_images

The global market for cavity bandpass filters is experiencing robust growth, driven by increasing demand across diverse sectors such as aerospace, wireless communication, and satellite communications. While precise market size data for 2025 is unavailable, considering a plausible market size of $500 million in 2025, and a Compound Annual Growth Rate (CAGR) of 8% (a reasonable estimate based on industry growth trends in related RF/microwave components), the market is projected to reach approximately $800 million by 2033. This growth is fueled by the miniaturization of electronics, the rise of 5G and beyond 5G technologies, and the ongoing expansion of satellite constellations for communication and Earth observation. The increasing need for high-frequency, high-performance filters in these applications is driving the demand for advanced cavity bandpass filter technologies. Specific growth within the various frequency bands (1GHz Below, 1-10GHz, etc.) will likely vary, with higher frequency bands experiencing faster growth due to the demands of newer technologies. Significant regional variations are expected, with North America and Asia Pacific likely leading the market, owing to strong technological advancements and substantial investments in these regions. The market segmentation by application reveals a diverse landscape. RF microwave applications remain a cornerstone, followed by substantial growth in radar T/R components due to advancements in radar systems for both civilian and military use. The aerospace sector's demand for reliable and high-performance filters in communication and navigation systems represents another significant market driver. Constraints on market growth could include the relatively high cost of advanced filter technologies and the competitive landscape marked by established players and emerging competitors. This necessitates innovation and cost optimization strategies for manufacturers to maintain competitiveness and achieve profitability. The strategic focus will be on developing filters with enhanced performance characteristics, miniaturization, and cost-effectiveness. Furthermore, collaborative efforts between filter manufacturers and end-users will be crucial for optimizing filter designs for specific applications, leading to sustained market expansion. This comprehensive report provides an in-depth analysis of the global cavity bandpass filters market, projecting a market value exceeding $2.5 billion by 2030. It delves into key market trends, growth drivers, challenges, and competitive landscapes, offering valuable insights for stakeholders across the RF microwave, aerospace, and satellite communication sectors.

Overview

This is a dataset to explore the effect of applying a new bias correction and quality filtering approach to increase the accuracy of atmospheric CO2 measurements derived from the Orbiting Carbon Observatory-2 (OCO-2) satellite. This is not an official OCO-2 data product.

Data Description

The dataset contains OCO-2 retrieved XCO2 B11.2 that have been corrected and filtered with a new machine learning approach from 2014 to the end of 2024. There is one file for each year. The following variables are contained in the files:

sounding_id, xco2_ML*, xco2, xco2_x2019, xco2_quality_flag_ML*, xco2_quality_flag, bias_correction_uncert_ML*, xco2_uncertainty, latitude, longitude, time, land_water_indicator, operation_mode

• new variables that contain the new bias coorected xco2, quality flag, and uncertainty and are not contained in the official OCO-2 Lite Files.

xco2_ML: XCO2 Machine Learning corrected XCO2 on x2019 scale

xco2_quality_flag_ML: XCO2 ternary quality flag: 0 = best quality data, 1 = good quality data for increasing sounding throughput if needed, 2 = poor quality data (do not use)

bias_correction_uncert_ML: XCO2 bias correction uncertainty

For the full set of variables contained in the LiteFiles and description of each variable please refere to the data user guide of the official OCO-2 lite files: https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_FP_11.2r/summary?keywords=oco2

Current Bias Correction Approach

The operational bias correction method uses a multiple linear regression-like approach to adjust errors in XCO2 relative to elements of the state vector derived from the ACOS retrieval. Adjustments are currently made manually, guided by various success metrics like TCCON observation agreement, retrieval variability reduction, and flux model coherence.

New Bias Correction Approach

1. Computational Optimization: Replaces manual tuning with computational methods, enhancing transparency, traceability, and reproducibility.2. Non-linear Error Modeling: Allows more flexibility in modeling retrieval errors, reducing biases, particularly in previously unusable data.3. Independent Flux Inversion Models: Excludes flux inversion models from bias correction development, maintaining OCO-2 measurement independence.4. Quantified Correction Uncertainties: Provides uncertainty quantification for each bias correction at the per sounding level.

Data Usage

If you find anything unexpected in the data please report your findings to Steffen.mauceri@jpl.nasa.gov and william.r.keely@jpl.nasa.gov so we can resolve any issues.

Additional Resources and Citation

Two preprints are currently available that describe the approach in detail and should be cited if the data is used. We will update the citations as soon as the papers are published:

https://doi.org/10.22541/essoar.174164198.80749970/v1https://doi.org/10.22541/essoar.174164203.37422284/v1

Copyright statement: © 2023 California Institute of Technology. Government sponsorship acknowledged.

The global aircraft filters market size is expected to reach USD 1,040.4 million by 2028 according to a new study by Polaris Market Research.

This repository contains the code used for the study: "Variant Filters Using Segregation Information Improve Mapping of Nectar-Production Genes in Sunflower (Helianthus annuus L.)". The study evaluates the impact of biologically informed variant filtering strategies on QTL mapping, demonstrating improved identification of candidate genes related to nectar production.ContentsCandidateGeneGetter.shThis shell script extracts candidate genes from a GFF annotation file (HAN412_Eugene_curated_v1_1.gff3) based on genomic regions specified in the Windows file. For each region (defined by chromosome, start position, and end position), it identifies all genes falling entirely within that window, counts them, and outputs the region information along with a comma-separated list of gene IDs to AshleyCandidateGenes.txt.Chi_square_template.RThis R script filters genomic markers using a chi-square test based on expected segregation ratios. The script is designed as a template that can be adjusted for different population types by modifying the expected ratios. The default values (48.4375% homozygous for each allele and 3.125% heterozygous) are set for F6 inbred lines, but can be modified to match the segregation expectations of any population being filtered. It retains markers whose observed genotype frequencies do not significantly deviate from expectations (p > 0.1), removing markers with segregation distortion that could interfere with accurate QTL identification.mapping.RThis R script performs QTL (Quantitative Trait Locus) mapping using the qtl package. It includes code for three distinct "Approaches," likely representing analyses performed on different datasets or using varied marker filtering strategies (Approach1.csv, Approach2.csv, Approach3.csv). The script covers data loading, genetic map estimation and refinement (including custom marker thinning functions and visualization of recombination frequencies), calculation of genotype probabilities, performing 1D (scanone), Composite Interval (cim), and 2D (scantwo) QTL scans, significance testing via permutations, and refining QTL models (fitqtl, refineqtl).marker_filt_dist.RThis R script filters genomic markers from a VCF file by removing markers within 125,000 bp of each other. It optimizes marker density while maintaining genome-wide coverage, ensuring the filtered set is suitable for QTL mapping and identifying genomic regions linked to nectar-production traits in sunflower.proc freq marker data.sasThis SAS script filters genetic markers based on segregation patterns. It utilizes PROC FREQ to calculate genotype frequencies for biallelic markers (assuming three genotype classes) and performs chi-square tests against expected segregation ratios (e.g., specified test probabilities like 0.484375, 0.03125, 0.484375, corresponding to F6 expectations). Markers significantly deviating from these expectations (p < 0.10 in this script) are identified and potentially excluded from downstream analyses, similar in principle to Chi_square_template.R but implemented within the SAS environment for specific datasets (markers.bialw).thinning_loop.RThis R script thins genomic markers based on inter-marker distance thresholds, identifying and removing redundant or closely spaced markers. It helps refine marker sets to balance genome coverage and computational efficiency, improving QTL mapping precision in the study of sunflower nectar-production traits. (Note: Similar custom functions are also included within mapping.R).WindowsThis plain text file serves as input for the CandidateGeneGetter.sh script. Each line defines a genomic window with three columns: Chromosome, Start Position, and End Position. These windows likely represent regions of interest identified through QTL mapping or other analyses.CitationBarstow, A.C., McNellie, J.P., Smart, B.C., Keepers, K.G., Prasifka, J.R., Kane, N.C., & Hulke, B.S. (2025). Variant filters using segregation information improve mapping of nectar-production genes in sunflower (Helianthus annuus L.). The Plant Genome.

The global Cavity Bandpass Filters market size was valued at USD XX million in 2025 and is projected to reach USD XX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The market growth is attributed to increasing applications in defense and aerospace industries, growing demand for high-frequency communication systems, and advancements in wireless technologies. The rise in satellite communications and the adoption of 5G networks further drive market expansion. Key market trends include the increasing popularity of 1-10GHz and 10-20GHz frequency bands due to their optimal performance in radar systems and wireless communication applications. Furthermore, the growing adoption of cavity bandpass filters in high-power applications, such as radar T/R components and satellite communications, is expected to boost market growth. Major players in the market include BSC, Smiths Interconnect, Telewave, Inc., Reactel, and Sinclair Technologies (Norsat International). These companies are investing in research and development to enhance the performance and functionality of cavity bandpass filters, leading to increased competition and innovation in the market. The global cavity bandpass filters market is expected to reach USD 1.5 million by 2026, growing at a CAGR of 5.5% during the forecast period. The market is driven by the increasing demand for cavity bandpass filters in RF microwave, radar T/R components, aerospace, wireless communication, and satellite communications applications.

File List SupplementRcode.txt Description The file SupplementRcode.txt is a plain text file containing R code for the method.

The humidifying air filter market is experiencing robust growth, driven by increasing awareness of indoor air quality and the health benefits of humidified air. The market, currently valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by several key factors, including rising prevalence of respiratory illnesses exacerbated by dry air, increasing disposable incomes leading to higher spending on home comfort and health products, and the growing adoption of smart home technologies that integrate humidifying air filters. The market segmentation is likely diverse, encompassing various filter types (HEPA, activated carbon, etc.), humidification technologies (evaporative, ultrasonic), and product applications (residential, commercial). Major players like Holmes, Trane, Essick Air, Oreck, Philips, Big R, and Honeywell are actively shaping the market landscape through product innovation and strategic partnerships. The market's expansion is expected to continue due to a rising demand for energy-efficient humidifiers and the development of advanced filter technologies with enhanced purification capabilities. However, potential restraints include fluctuations in raw material prices and the increasing competition from alternative air purification solutions. Regional variations in market penetration are anticipated, with developed economies exhibiting higher adoption rates initially, followed by growth in emerging markets as awareness and affordability increase. The continued focus on health and wellness, coupled with technological advancements, paints a positive outlook for the humidifying air filter market over the next decade. This comprehensive report provides an in-depth analysis of the global humidifying air filter market, projected to reach a valuation exceeding $2 billion by 2030. It delves into market concentration, key trends, dominant regions, product insights, and the competitive landscape, offering valuable insights for businesses and investors. The report leverages extensive market research and data analysis to forecast future growth and identify lucrative opportunities.

This archive contains the data and scripts necessary to reproduce the statistical analyses of the study Biotic filtering by species’ interactions constrains food-web variability across spatial and abiotic gradients.

Food-web and environmental data were obtained from the GATEWAy (1.0) database: https://idata.idiv.de/ddm/Data/ShowData/283?version=3. Food-web topolgical metrics were calculated using the script allMetBB.R. Jaccard dissimilarity was calculated using the R package vegan. Structural Equation Models (SEMs) were performed using the R package lavaan.

This archive contains also the script src/sampling-error.R, which reproduce potential sampling errors in species composition through a Monte Carlo approach. The data file data/samling-error.csv contains the raw data generated from the Monte Carlo and can be loaded to reproduce the sensitivity analyses. The script SEM_bootstrap.R reproduces the bootstrap analyses for the SEMs.

MATLAS (Mass Assembly of early-Type GaLAxies with their fine Structures) investigates the mass assembly of Early-Type Galaxies (ETGs) and the build-up of their scaling relations, with extremely deep optical images. The stellar populations in the outermost regions of ETGs, the fine structures (tidal tails, stellar stream, and shells) around them, the Globular Cluster (GCs) and dwarf satellites, preserve a record of past merger events and more generally of the evolution and transformation of galaxies. The MATLAS color HiPS has been generated from i, r, g HiPS.