Normalize data

The CSV dataset contains sentence pairs for a text-to-text transformation task: given a sentence that contains 0..n abbreviations, rewrite (normalize) the sentence in full words (word forms).

Training dataset: 64,665 sentence pairs Validation dataset: 7,185 sentence pairs. Testing dataset: 7,984 sentence pairs.

All sentences are extracted from a public web corpus (https://korpuss.lv/id/Tīmeklis2020) and contain at least one medical term.

Data normalization is a crucial step in the gene expression analysis as it ensures the validity of its downstream analyses. Although many metrics have been designed to evaluate the existing normalization methods, different metrics or different datasets by the same metric yield inconsistent results, particularly for the single-cell RNA sequencing (scRNA-seq) data. The worst situations could be that one method evaluated as the best by one metric is evaluated as the poorest by another metric, or one method evaluated as the best using one dataset is evaluated as the poorest using another dataset. Here raises an open question: principles need to be established to guide the evaluation of normalization methods. In this study, we propose a principle that one normalization method evaluated as the best by one metric should also be evaluated as the best by another metric (the consistency of metrics) and one method evaluated as the best using scRNA-seq data should also be evaluated as the best using bulk RNA-seq data or microarray data (the consistency of datasets). Then, we designed a new metric named Area Under normalized CV threshold Curve (AUCVC) and applied it with another metric mSCC to evaluate 14 commonly used normalization methods using both scRNA-seq data and bulk RNA-seq data, satisfying the consistency of metrics and the consistency of datasets. Our findings paved the way to guide future studies in the normalization of gene expression data with its evaluation. The raw gene expression data, normalization methods, and evaluation metrics used in this study have been included in an R package named NormExpression. NormExpression provides a framework and a fast and simple way for researchers to select the best method for the normalization of their gene expression data based on the evaluation of different methods (particularly some data-driven methods or their own methods) in the principle of the consistency of metrics and the consistency of datasets.

1. Microbiome sequencing data often need to be normalized due to differences in read depths, and recommendations for microbiome analyses generally warn against using proportions or rarefying to normalize data and instead advocate alternatives, such as upper quartile, CSS, edgeR-TMM, or DESeq-VS. Those recommendations are, however, based on studies that focused on differential abundance testing and variance standardization, rather than community-level comparisons (i.e., beta diversity), Also, standardizing the within-sample variance across samples may suppress differences in species evenness, potentially distorting community-level patterns. Furthermore, the recommended methods use log transformations, which we expect to exaggerate the importance of differences among rare OTUs, while suppressing the importance of differences among common OTUs. 2. We tested these theoretical predictions via simulations and a real-world data set. 3. Proportions and rarefying produced more accurate comparisons among communities and were the only methods that fully normalized read depths across samples. Additionally, upper quartile, CSS, edgeR-TMM, and DESeq-VS often masked differences among communities when common OTUs differed, and they produced false positives when rare OTUs differed. 4. Based on our simulations, normalizing via proportions may be superior to other commonly used methods for comparing ecological communities.

Dr. Kevin Bronson provides a second year of nitrogen and water management in wheat agricultural research dataset for compute. Ten irrigation treatments from a linear sprinkler were combined with nitrogen treatments. This dataset includes notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, including laboratory analysis results generated during the experimentation, plus high resolution plot level intermediate data tables of SAS process output, as well as the complete raw data sensor records and logger outputs.

This proximal terrestrial high-throughput plant phenotyping data examples our early tri-metric field method, where a geo-referenced 5Hz crop canopy height, temperature and spectral signature are recorded coincident to indicate a plant health status. In this development period, our Proximal Sensing Cart Mark1 (PSCM1) platform suspends a single cluster of sensors on a dual sliding vertical placement armature.

Experimental design and operational details of research conducted are contained in related published articles, however further description of the measured data signals as well as germane commentary is herein offered.

The primary component of this dataset is the Holland Scientific (HS) CropCircle ACS-470 reflectance numbers. Which as derived here, consist of raw active optical band-pass values, digitized onboard the sensor product. Data is delivered as sequential serialized text output including the associated GPS information. Typically this is a production agriculture support technology, enabling an efficient precision application of nitrogen fertilizer. We used this optical reflectance sensor technology to investigate plant agronomic biology, as the ACS-470 is a unique performance product being not only rugged and reliable but illumination active and filter customizable.

Individualized ACS-470 sensor detector behavior and subsequent index calculation influence can be understood through analysis of white-panel and other known target measurements. When a sensor is held 120cm from a titanium dioxide white painted panel, a normalized unity value of 1.0 is set for each detector. To generate this dataset we used a Holland Scientific SC-1 device and set the 1.0 unity value (field normalize) on each sensor individually, before each data collection, and without using any channel gain boost. The SC-1 field normalization device allows a communications connection to a Windows machine, where company provided sensor control software enables the necessary sensor normalization routine, and a real-time view of streaming sensor data.

This type of active proximal multi-spectral reflectance data may be perceived as inherently “noisy”; however basic analytical description consistently resolves a biological patterning, and more advanced statistical analysis is suggested to achieve discovery. Sources of polychromatic reflectance are inherent in the environment; and can be influenced by surface features like wax or water, or presence of crystal mineralization; varying bi-directional reflectance in the proximal space is a model reality, and directed energy emission reflection sampling is expected to support physical understanding of the underling passive environmental system.

Soil in view of the sensor does decrease the raw detection amplitude of the target color returned and can add a soil reflection signal component. Yet that return accurately represents a largely two-dimensional cover and intensity signal of the target material present within each view. It does however, not represent a reflection of the plant material solely because it can contain additional features in view. Expect NDVI values greater than 0.1 when sensing plants and saturating more around 0.8, rather than the typical 0.9 of passive NDVI.

The active signal does not transmit energy to penetrate, perhaps past LAI 2.1 or less, compared to what a solar induced passive reflectance sensor would encounter. However the focus of our active sensor scan is on the uppermost expanded canopy leaves, and they are positioned to intercept the major solar energy. Active energy sensors are more easy to direct, and in our capture method we target a consistent sensor height that is 1m above the average canopy height, and maintaining a rig travel speed target around 1.5 mph, with sensors parallel to earth ground in a nadir view.

We consider these CropCircle raw detector returns to be more “instant” in generation, and “less-filtered” electronically, while onboard the “black-box” device, than are other reflectance products which produce vegetation indices as averages of multiple detector samples in time.

It is known through internal sensor performance tracking across our entire location inventory, that sensor body temperature change affects sensor raw detector returns in minor and undescribed yet apparently consistent ways.

Holland Scientific 5Hz CropCircle active optical reflectance ACS-470 sensors, that were measured on the GeoScout digital propriety serial data logger, have a stable output format as defined by firmware version. Fifteen collection events are presented.

Different numbers of csv data files were generated based on field operations, and there were a few short duration instances where GPS signal was lost. Multiple raw data files when present, including white panel measurements before or after field collections, were combined into one file, with the inclusion of the null value placeholder -9999. Two CropCircle sensors, numbered 2 and 3, were used, supplying data in a lined format, where variables are repeated for each sensor. This created a discrete data row for each individual sensor measurement instance.

We offer six high-throughput single pixel spectral colors, recorded at 530, 590, 670, 730, 780, and 800nm. The filtered band-pass was 10nm, except for the NIR, which was set to 20 and supplied an increased signal (including an increased noise).

Dual, or tandem approach, CropCircle paired sensor usage empowers additional vegetation index calculations, such as:
DATT = (r800-r730)/(r800-r670)
DATTA = (r800-r730)/(r800-r590)
MTCI = (r800-r730)/(r730-r670)
CIRE = (r800/r730)-1
CI = (r800/r590)-1
CCCI = NDRE/NDVIR800
PRI = (r590-r530)/(r590+r530)
CI800 = ((r800/r590)-1)
CI780 = ((r780/r590)-1)

The Campbell Scientific (CS) environmental data recording of small range (0 to 5 v) voltage sensor signals are accurate and largely shielded from electronic thermal induced influence, or other such factors by design. They were used as was descriptively recommended by the company. A high precision clock timing, and a recorded confluence of custom metrics, allow the Campbell Scientific raw data signal acquisitions a high research value generally, and have delivered baseline metrics in our plant phenotyping program. Raw electrical sensor signal captures were recorded at the maximum digital resolution, and could be re-processed in whole, while the subsequent onboard calculated metrics were often data typed at a lower memory precision and served our research analysis.

Improved Campbell Scientific data at 5Hz is presented for nine collection events, where thermal, ultrasonic displacement, and additional GPS metrics were recorded. Ultrasonic height metrics generated by the Honeywell sensor and present in this dataset, represent successful phenotypic recordings. The Honeywell ultrasonic displacement sensor has worked well in this application because of its 180Khz signal frequency that ranges 2m space. Air temperature is still a developing metric, a thermocouple wire junction (TC) placed in free air with a solar shade produced a low-confidence passive ambient air temperature.

Campbell Scientific logger derived data output is structured in a column format, with multiple sensor data values present in each data row. One data row represents one program output cycle recording across the sensing array, as there was no onboard logger data averaging or down sampling. Campbell Scientific data is first recorded in binary format onboard the data logger, and then upon data retrieval, converted to ASCII text via the PC based LoggerNet CardConvert application. Here, our full CS raw data output, that includes a four-line header structure, was truncated to a typical single row header of variable names. The -9999 placeholder value was inserted for null instances.

There is canopy thermal data from three view vantages. A nadir sensor view, and looking forward and backward down the plant row at a 30 degree angle off nadir. The high confidence Apogee Instruments SI-111 type infrared radiometer, non-contact thermometer, serial number 1022 was in a front position looking forward away from the platform, number 1023 with a nadir view was in middle position, and sensor number 1052 was in a rear position and looking back toward the platform frame. We have a long and successful history testing and benchmarking performance, and deploying Apogee Instruments infrared radiometers in field experimentation. They are biologically spectral window relevant sensors and return a fast update 0.2C accurate average surface temperature, derived from what is (geometrically weighted) in their field of view.

Data gaps do exist beyond null value -9999 designations, there are some instances when GPS signal was lost, or rarely on HS GeoScout logger error. GPS information may be missing at the start of data recording. However once the receiver supplies a signal the values will populate. Likewise there may be missing information at the end of a data collection, where the GPS signal was lost but sensors continue to record along with the data logger timestamping.

In the raw CS data, collections 1 through 7 are represented by only one table file, where the UTC from the GPS

This workshop will introduce OpenRefine, a powerful open source tool for exploring, cleaning and manipulating "messy" data. Through hands-on activities, using a variety of datasets, participants will learn how to: Explore and identify patterns in data; Normalize data using facets and clusters; Manipulate and generate new textual and numeric data; Transform and reshape datasets; Use the General Regular Expression Language (GREL) to undertake manipulations, such as concatenating strings.

This archive contains affymetrix files necessary to normalize microarrays data and modified annotations files required in GIANT APT-Normalize tool for annotation of normalized data.

Reference genes used in normalizing qRT-PCR data are critical for the accuracy of gene expression analysis. However, many traditional reference genes used in zebrafish early development are not appropriate because of their variable expression levels during embryogenesis. In the present study, we used our previous RNA-Seq dataset to identify novel reference genes suitable for gene expression analysis during zebrafish early developmental stages. We first selected 197 most stably expressed genes from an RNA-Seq dataset (29,291 genes in total), according to the ratio of their maximum to minimum RPKM values. Among the 197 genes, 4 genes with moderate expression levels and the least variation throughout 9 developmental stages were identified as candidate reference genes. Using four independent statistical algorithms (delta-CT, geNorm, BestKeeper and NormFinder), the stability of qRT-PCR expression of these candidates was then evaluated and compared to that of actb1 and actb2, two commonly used zebrafish reference genes. Stability rankings showed that two genes, namely mobk13 (mob4) and lsm12b, were more stable than actb1 and actb2 in most cases. To further test the suitability of mobk13 and lsm12b as novel reference genes, they were used to normalize three well-studied target genes. The results showed that mobk13 and lsm12b were more suitable than actb1 and actb2 with respect to zebrafish early development. We recommend mobk13 and lsm12b as new optimal reference genes for zebrafish qRT-PCR analysis during embryogenesis and early larval stages.

The values in this raster are unit-less scores ranging from 0 to 1 that represent normalized dollars per acre damage claims from antelope on Wyoming lands. This raster is one of 9 inputs used to calculate the "Normalized Importance Index."

A data set used to normalize the detector response of the ARCS instrument see ARCS_269548.md in the data set for more details.

The technological advances in mass spectrometry allow us to collect more comprehensive data with higher quality and increasing speed. With the rapidly increasing amount of data generated, the need for streamlining analyses becomes more apparent. Proteomics data is known to be often affected by systemic bias from unknown sources, and failing to adequately normalize the data can lead to erroneous conclusions. To allow researchers to easily evaluate and compare different normalization methods via a user-friendly interface, we have developed “proteiNorm”. The current implementation of proteiNorm accommodates preliminary filters on peptide and sample levels followed by an evaluation of several popular normalization methods and visualization of the missing value. The user then selects an adequate normalization method and one of the several imputation methods used for the subsequent comparison of different differential expression methods and estimation of statistical power. The application of proteiNorm and interpretation of its results are demonstrated on two tandem mass tag multiplex (TMT6plex and TMT10plex) and one label-free spike-in mass spectrometry example data set. The three data sets reveal how the normalization methods perform differently on different experimental designs and the need for evaluation of normalization methods for each mass spectrometry experiment. With proteiNorm, we provide a user-friendly tool to identify an adequate normalization method and to select an appropriate method for differential expression analysis.

Raw data used to normalize detector performance on the ARCS instrument For the run cycle starting in June 2023. This is a V cylinder and the T0 chopper is set to 150 Hz and phased for 300 meV. All Fermi Choppers are out of the Beam.

Data and supplementary information for the paper entitled "Adapting Phrase-based Machine Translation to Normalise Medical Terms in Social Media Messages" to be published at EMNLP 2015: Conference on Empirical Methods in Natural Language Processing — September 17–21, 2015 — Lisboa, Portugal.

ABSTRACT: Previous studies have shown that health reports in social media, such as DailyStrength and Twitter, have potential for monitoring health conditions (e.g. adverse drug reactions, infectious diseases) in particular communities. However, in order for a machine to understand and make inferences on these health conditions, the ability to recognise when laymen's terms refer to a particular medical concept (i.e. text normalisation) is required. To achieve this, we propose to adapt an existing phrase-based machine translation (MT) technique and a vector representation of words to map between a social media phrase and a medical concept. We evaluate our proposed approach using a collection of phrases from tweets related to adverse drug reactions. Our experimental results show that the combination of a phrase-based MT technique and the similarity between word vector representations outperforms the baselines that apply only either of them by up to 55%.

A data set used to normalize the detector response of the ARCS instrument see ARCS_226797.md in the data set for more details.

Neutron scattering Data from a Vanadium cylinder. Acquired on the ARCS spectrometer in white beam mode to normalize the detector efficiencies. During Cycle 2022B

One of the body fluids often used in metabolomics studies is urine. The peak intensities of metabolites in urine are affected by the urine history of an individual resulting in dilution differences. This requires therefore normalization of the data to correct for such differences. Two normalization techniques are commonly applied to urine samples prior to their further statistical analysis. First, AUC normalization aims to normalize a group of signals with peaks by standardizing the area under the curve (AUC) within a sample to the median, mean or any other proper representation of the amount of dilution. The second approach uses specific end-product metabolites such as creatinine and all intensities within a sample are expressed relative to the creatinine intensity. Another way of looking at urine metabolomics data is by realizing that the ratios between peak intensities are the information-carrying features. This opens up possibilities to use another class of data analysis techniques designed to deal with such ratios: compositional data analysis. In this approach special transformations are defined to deal with the ratio problem. In essence, it comes down to using another distance measure than the Euclidian Distance that is used in the conventional analysis of metabolomics data. We will illustrate using this type of approach in combination with three-way methods (i.e. PARAFAC) to be used in cases where samples of some biological material are measured at multiple time points. Aim of the paper is to develop PARAFAC modeling of three-way metabolomics data in the context of compositional data and compare this with standard normalization techniques for the specific case of urine metabolomics data.

Genome-wide analysis of gene expression or protein binding patterns using different array or sequencing based technologies is now routinely performed to compare different populations, such as treatment and reference groups. It is often necessary to normalize the data obtained to remove technical variation introduced in the course of conducting experimental work, but standard normalization techniques are not capable of eliminating technical bias in cases where the distribution of the truly altered variables is skewed, i.e. when a large fraction of the variables are either positively or negatively affected by the treatment. However, several experiments are likely to generate such skewed distributions, including ChIP-chip experiments for the study of chromatin, gene expression experiments for the study of apoptosis, and SNP-studies of copy number variation in normal and tumour tissues. A preliminary study using spike-in array data established that the capacity of an experiment to identify altered variables and generate unbiased estimates of the fold change decreases as the fraction of altered variables and the skewness increases. We propose the following work-flow for analyzing high-dimensional experiments with regions of altered variables: (1) Pre-process raw data using one of the standard normalization techniques. (2) Investigate if the distribution of the altered variables is skewed. (3) If the distribution is not believed to be skewed, no additional normalization is needed. Otherwise, re-normalize the data using a novel HMM-assisted normalization procedure. (4) Perform downstream analysis. Here, ChIP-chip data and simulated data were used to evaluate the performance of the work-flow. It was found that skewed distributions can be detected by using the novel DSE-test (Detection of Skewed Experiments). Furthermore, applying the HMM-assisted normalization to experiments where the distribution of the truly altered variables is skewed results in considerably higher sensitivity and lower bias than can be attained using standard and invariant normalization methods.

Sentinel Hub NBR description: To detect burned areas, the NBR-RAW index is the most appropriate choice. Using bands 8 and 12 it highlights burnt areas in large fire zones greater than 500 acres. To observe burn severity, you may subtract the post-fire NBR image from the pre-fire NBR image. Darker pixels indicate burned areas.

NBR = (NIR – SWIR) / (NIR + SWIR)

Sentinel-2 NBR = (B08 - B12) / (B08 + B12)

These data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital & Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat condition at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains the following indices derived from Defra and JNCC Sentinel-2 Analysis Ready Data.

NDVI, NDMI, NDWI, NBR, and EVI files are generated for the following Sentinel-2 granules: • T30UWE • T30UXF • T30UWF • T30UXE • T31UCV • T30UYE • T31UCA

As the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.

Normalised Digital Surface Model - 1m resolution. The dataset contains the Normalised Digital Surface Model for the Washington Area.Voids exist in the data due to data redaction conducted under the guidance of the United States Secret Service. All lidar data returns and collected data were removed from the dataset based on the redaction footprint shapefile generated in 2017.

BackgroundGene expression analysis is an essential part of biological and medical investigations. Quantitative real-time PCR (qPCR) is characterized with excellent sensitivity, dynamic range, reproducibility and is still regarded to be the gold standard for quantifying transcripts abundance. Parallelization of qPCR such as by microfluidic Taqman Fluidigm Biomark Platform enables evaluation of multiple transcripts in samples treated under various conditions. Despite advanced technologies, correct evaluation of the measurements remains challenging. Most widely used methods for evaluating or calculating gene expression data include geNorm and ΔΔCt, respectively. They rely on one or several stable reference genes (RGs) for normalization, thus potentially causing biased results. We therefore applied multivariable regression with a tailored error model to overcome the necessity of stable RGs.ResultsWe developed a RG independent data normalization approach based on a tailored linear error model for parallel qPCR data, called LEMming. It uses the assumption that the mean Ct values within samples of similarly treated groups are equal. Performance of LEMming was evaluated in three data sets with different stability patterns of RGs and compared to the results of geNorm normalization. Data set 1 showed that both methods gave similar results if stable RGs are available. Data set 2 included RGs which are stable according to geNorm criteria, but became differentially expressed in normalized data evaluated by a t-test. geNorm-normalized data showed an effect of a shifted mean per gene per condition whereas LEMming-normalized data did not. Comparing the decrease of standard deviation from raw data to geNorm and to LEMming, the latter was superior. In data set 3 according to geNorm calculated average expression stability and pairwise variation, stable RGs were available, but t-tests of raw data contradicted this. Normalization with RGs resulted in distorted data contradicting literature, while LEMming normalized data did not.ConclusionsIf RGs are coexpressed but are not independent of the experimental conditions the stability criteria based on inter- and intragroup variation fail. The linear error model developed, LEMming, overcomes the dependency of using RGs for parallel qPCR measurements, besides resolving biases of both technical and biological nature in qPCR. However, to distinguish systematic errors per treated group from a global treatment effect an additional measurement is needed. Quantification of total cDNA content per sample helps to identify systematic errors.