A Baseflow Filter for Hydrologic Models in R Resources in this dataset:Resource Title: A Baseflow Filter for Hydrologic Models in R. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=383&modecode=20-72-05-00 download page

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.

Dataset Card for Annotated LibriTTS-R

This dataset is an annotated version of a filtered LibriTTS-R [1]. LibriTTS-R [1] is a sound quality improved version of the LibriTTS corpus which is a multi-speaker English corpus of approximately 960 hours of read English speech at 24kHz sampling rate, published in 2019. In the text_description column, it provides natural language annotations on the characteristics of speakers and utterances, that have been generated using the Data-Speech… See the full description on the dataset page: https://huggingface.co/datasets/parler-tts/libritts-r-filtered-speaker-descriptions.

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.

Data on vegetated filter strips, sediment loading into and out of riparian corridors/buffers (VFS), removal efficiency of sediment, meta-analysis of removal efficiencies, dimensional analysis of predictor variables, and regression modeling of VFS removal efficiencies. This dataset is associated with the following publication: Ramesh, R., L. Kalin, M. Hantush, and A. Chaudhary. A secondary assessment of sediment trapping effectiveness by vegetated buffers. ECOLOGICAL ENGINEERING. Elsevier Science Ltd, New York, NY, USA, 159: 106094, (2021).

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.

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.

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.

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

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).

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).

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

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

post train nemotron dataset filtered for english only and reasoning on entries

Global Pool and Spa Filter Cartridge Market is poised to witness substantial growth, reaching a value of USD 2.75 Billion by the year 2033, up from USD 1.55 Billion attained in 2024. The market is anticipated to display a Compound Annual Growth Rate (CAGR) of 6.56% between 2025 and 2033.

The Global Pool and Spa Filter Cartridge market size to cross USD 2.75 Billion in 2033. [https://edison.valuem

od2025/libritts-r-filtered-speaker-descriptions-augmented dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

This paper develops a new model for the analysis of stochastic volatility (SV) models. Since volatility is a latent variable in SV models, it is difficult to evaluate the exact likelihood. In this paper, a non-linear filter which yields the exact likelihood of SV models is employed. Solving a series of integrals in this filter by piecewise linear approximations with randomly chosen nodes produces the likelihood, which is maximized to obtain estimates of the SV parameters. A smoothing algorithm for volatility estimation is also constructed. Monte Carlo experiments show that the method performs well with respect to both parameter estimates and volatility estimates. We illustrate our model by analysing daily stock returns on the Tokyo Stock Exchange. Since the method can be applied to more general models, the SV model is extended so that several characteristics of daily stock returns are allowed, and this more general model is also estimated.