Report Filter Definitions and Guidance Please note that all filter options are present in the dataset. For example, if you are looking at a dataset and a state is missing, it means there is no data for the year selected in that state - it does not use a list of all US states. Also note that if the data table disappears, there is no data available for the filter selections made.

Filtered WIT, an Image-Text Dataset.

A reliable Dataset to run Image-Text models. You can find WIT, Wikipedia Image Text Dataset, here Data was taken from dalle-mini/wit

  Author

Aarush Katta

  Data Structure

The data is stored as tars, containing 10,000 samples per tar. The parquets contain the metadata of each tar, which was crated using this script Each tar contains a .jpg, .txt, and .json. The image is stored in .jpg, the caption in .txt. and the metadata in… See the full description on the dataset page: https://huggingface.co/datasets/laion/filtered-wit.

Dataset Card for No Language Left Behind (NLLB - 200vo)

  Dataset Summary

This dataset was created based on metadata for mined bitext released by Meta AI. It contains bitext for 148 English-centric and 1465 non-English-centric language pairs using the stopes mining library and the LASER3 encoders (Heffernan et al., 2022). The complete dataset is ~450GB. CCMatrix contains previous versions of mined instructions.

  How to use the data

There are two ways… See the full description on the dataset page: https://huggingface.co/datasets/yaya-sy/nllb-filtering.

Model-based prognostics approaches use domain knowledge about a system and its failure modes through the use of physics-based models. Model-based prognosis is generally divided into two sequential problems: a joint state-parameter estimation problem, in which, using the model, the health of a system or component is determined based on the observations; and a prediction problem, in which, using the model, the state-parameter distribution is simulated forward in time to compute end of life and remaining useful life. The first problem is typically solved through the use of a state observer, or filter. The choice of filter depends on the assumptions that may be made about the system, and on the desired algorithm performance. In this paper, we review three separate filters for the solution to the first problem: the Daum filter, an exact nonlinear filter; the unscented Kalman filter, which approximates nonlinearities through the use of a deterministic sampling method known as the unscented transform; and the particle filter, which approximates the state distribution using a finite set of discrete, weighted samples, called particles. Using a centrifugal pump as a case study, we conduct a number of simulation-based experiments investigating the performance of the different algorithms as applied to prognostics.

Since these microarrays contained duplicated spots, the parentheses represent the number of unique spots or profiles in the dataset.

Russia Avg Consumer Price: Tobacco: Cigarettes: Foreign Brands: with Filter data was reported at 131.420 RUB/Pack in Jan 2019. This records an increase from the previous number of 130.140 RUB/Pack for Dec 2018. Russia Avg Consumer Price: Tobacco: Cigarettes: Foreign Brands: with Filter data is updated monthly, averaging 27.510 RUB/Pack from Jan 1995 (Median) to Jan 2019, with 289 observations. The data reached an all-time high of 131.420 RUB/Pack in Jan 2019 and a record low of 1.390 RUB/Pack in Jan 1995. Russia Avg Consumer Price: Tobacco: Cigarettes: Foreign Brands: with Filter data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PA007: Average Consumer Price: Tobacco.

Contains scans of a bin filled with different parts ( screws, nuts, rods, spheres, sprockets). For each part type, RGB image and organized 3D point cloud obtained with structured light sensor are provided. In addition, unorganized 3D point cloud representing an empty bin and a small Matlab script to read the files is also provided. 3D data contain a lot of outliers and the data were used to demonstrate a new filtering technique.

Dataset Card for "amazon-product-data-filter"

  Dataset Summary

The Amazon Product Dataset contains product listing data from the Amazon US website. It can be used for various NLP and classification tasks, such as text generation, product type classification, attribute extraction, image recognition and more.

  Languages

The text in the dataset is in English.

  Dataset Structure





  Data Instances

Each data point provides product information, such… See the full description on the dataset page: https://huggingface.co/datasets/iarbel/amazon-product-data-filter.

Check out our data lens page for additional data filtering and sorting options: https://data.cityofnewyork.us/view/i4p3-pe6a

This dataset contains Open Parking and Camera Violations issued by the City of New York. Updates will be applied to this data set on the following schedule:

New or open tickets will be updated weekly (Sunday). Tickets satisfied will be updated daily (Tuesday through Sunday). NOTE: Summonses that have been written-off are indicated by blank financials.

Summons images will not be available during scheduled downtime on Sunday - Monday from 1:00 am to 2:30 am and on Sundays from 5:00 am to 10:00 am.

• Initial dataset loaded 05/14/2016.

Bilateral Filtering

## Overview

Bilateral Filtering is a dataset for object detection tasks - it contains Nodules annotations for 280 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Spatio-temporal datasets are rapidly growing in size. For example, environmental variables are measured with increasing resolution by increasing numbers of automated sensors mounted on satellites and aircraft. Using such data, which are typically noisy and incomplete, the goal is to obtain complete maps of the spatio-temporal process, together with uncertainty quantification. We focus here on real-time filtering inference in linear Gaussian state-space models. At each time point, the state is a spatial field evaluated on a very large spatial grid, making exact inference using the Kalman filter computationally infeasible. Instead, we propose a multi-resolution filter (MRF), a highly scalable and fully probabilistic filtering method that resolves spatial features at all scales. We prove that the MRF matrices exhibit a particular block-sparse multi-resolution structure that is preserved under filtering operations through time. We describe connections to existing methods, including hierarchical matrices from numerical mathematics. We also discuss inference on time-varying parameters using an approximate Rao-Blackwellized particle filter, in which the integrated likelihood is computed using the MRF. Using a simulation study and a real satellite-data application, we show that the MRF strongly outperforms competing approaches. Supplementary materials include Python code for reproducing the simulations, some detailed properties of the MRF and auxiliary theoretical results.

Check out our data lens page for additional data filtering and sorting options: https://data.cityofnewyork.us/view/i4p3-pe6a

This dataset contains Open Parking and Camera Violations issued by the City of New York. Updates will be applied to this data set on the following schedule:

New or open tickets will be updated weekly (Sunday). Tickets satisfied will be updated daily (Tuesday through Sunday). NOTE: Summonses that have been written-off are indicated by blank financials.

Summons images will not be available during scheduled downtime on Sunday - Monday from 1:00 am to 2:30 am and on Sundays from 5:00 am to 10:00 am.

• Initial dataset loaded 05/14/2016.

Many diagnostic datasets suffer from the adverse effects of spikes that are embedded in data and noise. For example, this is true for electrical power system data where the switches, relays, and inverters are major contributors to these effects. Spikes are mostly harmful to the analysis of data in that they throw off real-time detection of abnormal conditions, and classification of faults. Since noise and spikes are mixed together and embedded within the data, removal of the unwanted signals from the data is not always easy and may result in losing the integrity of the information carried by the data. Additionally, in some applications noise and spikes need to be filtered independently. The proposed algorithm is a multi-resolution filtering approach based on Haar wavelets that is capable of removing spikes while incurring insignificant damage to other data. In particular, noise in the data, which is a useful indicator that a sensor is healthy and not stuck, can be preserved using our approach. Presented here is the theoretical background with some examples from a realistic testbed.

R was used for the pipeline. All R code is provided for the creation of simulated datasets and filtering of those datasets.

We've also provide .012 data input files (.txt) with their env files (.env) and the outputs of baypass (.csv) and lfmm (calpval).

The name of the outputs look like this: emsim_156_6_0.5_0.1.txt.lfmm_env_2.calpval This naming convention is the same throughout.

emsim = name of the datastet E. microcarpa simulation

156 = # of individuals i.e., sample size

6 = number of individuals per population

0.5 = the missing data threshold (note, for coding purposes this is actually the % of data kept : 10% missing data will be 0.9) (one of 0.5, 0.6, 0.7 0.8, or 0.9)

0.1 = minor allele frequency (one of 0.1, 0.05, or 0.01)

Associated SNPs

V#####MT - SNPs associated with BIO5

V#####MP - SNPs associated with BIO14

Dataset

This dataset was created by TW PROJECT

Contents

Abstract‘Filtering’, or the reduction in species diversity that occurs because not all species can persist in all locations, is thought to unfold hierarchically, controlled by the environment at large scales and competition at small scales. However, the ecological effects of competition and the environment are not independent, and observational approaches preclude investigation into their interplay. We use a demographic approach with 30 plant species to experimentally test (i) the effect of competition on species persistence in two soil moisture environments, and (ii) the effect of environmental conditions on mechanisms underlying competitive coexistence. We find that competitors cause differential species persistence across environments even when effects are lacking in the absence of competition, and that the traits that determine persistence depend on the competitive environment. If our study had been observational and trait-based, we would have erroneously concluded that the environment filters species with low biomass, shallow roots, and small seeds. Changing environmental conditions generated idiosyncratic effects on coexistence outcomes, increasing competitive exclusion of some species while promoting coexistence of others. Our results highlight the importance of considering environmental filtering in light of, rather than in isolation from, competition, and challenge community assembly models and approaches to projecting future species distributions. Usage notesGermain BL dataFirst worksheet includes the demographic data, second worksheet the trait data. Species codes are expanded in the supplementary materials.

Sequencing results from filtering raw sequence data from environmental DNA metabarcoding samples of River Thames fish communities.

Samples were collected from two sites during 2019 over 12 months from the Thames Basin, London, U.K., sampling a minimum of every week. Site 1. River Lee (freshwater) and site 2. Richmond Lock, Thames River (tidal). Samples were amplified with the primer set MiFish-U.

The file is an Excel workbook of the sequencing results from filtering the raw sequence data (file "Temporal_eDNA_GC-EC-9225.tar.gz") through the pipeline DADA2: providing ASV IDs, sample and ASV table with readcounts, and fish names.

For further information on filtering settings see the published paper.

Hallam J, Clare EL, Jones JI, Day JJ. (2023) Fine-scale environmental DNA metabarcoding provides rapid and effective monitoring of fish community dynamics. Environmental DNA. DOI:10.1002/edn3.486

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.

The different data sets analyzed and generated in this study. These data includes the raw data file (raw.csv), the data filtered by the optimized UMI count hresholds (opt_thr.csv), the data filtered by the UMI thresholds and HLA matching (hla_match.csv), and final filtered data including only GEMs with complete TCR annotation.

A complete list of live websites using the Wp Meta Data Filter And Taxonomy Filter technology, compiled through global website indexing conducted by WebTechSurvey.