Data Quality - Science topic

A few factors—such as how well the model is doing, how excellent the data is, and how much important information the data holds would help researcher decide whether to enhance the model or cleanup the data. Usually, if the model continues to underperform after testing several configurations, it is preferable to enhance the data by including more examples or greater details. But if the data is already strong, improving the model itself might be more effective. Best outcomes come from enhancing both over time, depending on what you discover from each stage. hope this helps.

We can measure the impact by some statistical methods as the correlation coeffecient between the companies' works results (as an independent variable) and the economic development results (as a dependent variables), we can analyse this correlation statistically by the autocorrelation of every company results. The quantitative measures are effective in these cases.

Broadly speaking GLMMs are fine with imbalance and short of getting more data there's not much better as an option in most cases.

They are however not magic. The big concern is simply lack of information where you have small cell sizes in something like a factorial design or just sparse information because of the imbalance. This leads to imprecise estimates (as it should - less information implies noisier estimates). In other words just because it copes well with imbalance doesn't mean you don't have low statistical power in relation to some research Qs. As a rule of thumb power to detect a difference in means is maximised when there is balance and limited more by the smaller n for imbalanced samples.

A full Bayesian model with informative priors might help with this but in my view that depends on being able to justify the priors.

GLMMs also have shrinkage - which is one one reason why they work well with imbalance. All estimates are shrunk towards the estimates for typical observations - this dampens down some of the noise in estimating with imbalance. In a sense they trade off precision for bias - the estimates are arguably more biased within sample but more precise than in models without shrinkage. They should also generalise better out of sample.

By addressing the challenges in data quality, system integration, security, scalability, and talent development, while fostering a culture of transparency and change management, production companies can unlock the full potential of AI. This holistic approach will enable manufacturers to drive operational efficiencies, improve product quality, and ultimately gain a competitive edge in the market. Friday Ameh

Data quality is crucial for accurate and reliable financial forecasting. High-quality data ensures precise predictions, while poor data can lead to flawed decision-making, financial mismanagement, and loss of trust. Accurate data supports budgeting, investment decisions, and risk assessments, whereas poor data risks causing inaccurate forecasts and financial instability.

Together, AI and blockchain can mitigate data quality issues, ensuring more accurate and reliable financial forecasting.

The multidimensional data quality indicators that I developed for the low-cost sensors is based on the outcome of my systematic literature review in the attachment.

For a software engineer, generative AI can assist with code generation. GitHub Copilot and ChatGPT are great tools for speeding up development. Another use case is bug detection and fixing. AI can analyze codes and identify and fix bugs. In other words, generative AI can be a personal assistance for software developers.

Great point! XAI is crucial for transparency and trust, especially in agriculture where understanding model predictions can help identify biases or errors caused by poor data quality. Thanks for highlighting that!

Hello, Ragavan,

Actually, there is no reason why Linf should be greater than Lmax.

Lmax may be above or below Linf (see Schwamborn, 2018).

Schwamborn, R. (2018). How reliable are the Powell–Wetherall plot method and the maximum-length approach? Implications for length-based studies of growth and mortality. Reviews in Fish Biology and Fisheries, 28(3), 587-605.

Within a simple VBGF (von Bertalanffy growth function) growth model, and considering a healthy, pristine population, a large number of individuals will reach adulthood and grow to a size that is close to the mean asymptotic size of the population Linf (i.e., the theoretical mean size at infinite age). Some old individuals may be larger than Linf, and others smaller than Linf. The mean length of the old individuals should be somewhere below Linf. This does not mean that all individuals are smaller than Linf.

Within such a population, the Lmax/Linf ratio will be defined by the intra-population variability in Linf (Schwamborn, 2018), and the mortality/growth ratio (Z/K ratio).

If all individuals were much smaller than Linf, this would indicate a "dwarfed" population (Schwamborn & Schwamborn, 2021, https://panamjas.org/pdf_artigos/PANAMJAS_16(1)_57-78.pdf), e.g., due to growth overfishing.

Don't forget that it is important to estimate Linf and K within a near-unconstrained search space (not constraining or fixing Linf a priori), by using search spaces that are as wide as possible, where the search space limits will not interfere with your fit algorithm.

Also, you should ideally repeat the ELEFAN_GA model fit procedure many, many times, with different “seed” values, to avoid being stuck in local maxima within the response surface. You can also do this in an automatized way, hundreds of times, and obtain 95% confidence intervals for Linf and K by bootstrapping with the "fishboot" package (using the function ELEFAN_GA_boot, Schwamborn et al., 2019)

Schwamborn, R., Mildenberger, T. K., & Taylor, M. H. (2019). Assessing sources of uncertainty in length-based estimates of body growth in populations of fishes and macroinvertebrates with bootstrapped ELEFAN. Ecological Modelling, 393, 37-51.

#To load fishboot (using devtools):

library(devtools) install_github("rschwamborn/fishboot")

Good luck with your analyses. Don't hesitate to contact me if you need any help.

Best regards,

Ralf

--

************************************************************************

Prof. Dr. Ralf Schwamborn

Depto. de Oceanografia

Universidade Federal de Pernambuco (UFPE)

50670-901 Recife-PE

Brazil

Tel.: +55-(0)81 – 2126-8225, 81-2126-8226

CV: http://lattes.cnpq.br/8733048825246392

E-mail: ralf.schwamborn(at)ufpe.br, rschwamborn(at)gmx.net

************************************************************************

see https://www.researchgate.net/post/Is_the_linear_regression_the_best_fit_for_this_study

Not sure why you are re-asking this question.

Thank you for your answear. Honestly, I cannot fully understand what you are mentioning. Would you please give me some examples or documents? Deborah J Hilton

Hi Hana,

You have several ways to proceed. The first one is to use the worrms package (https://docs.ropensci.org/worrms/articles/worrms.html). This package can be a good alternative. The second one, which is a bit more complex, is to request an API key from the administrator of the Algaebase website (https://www.algaebase.org/api/). Afterwards, you will need to create a function in R that allows you to retrieve the information in JSON format and convert it into a dataframe. By the end of the year, I will post on my GitHub the functions that I have created/used to update my data.

Alternatively, you also have https://docs.ropensci.org/taxize/index.html. You can find a lot of information on this website as well: https://www.marinedatascience.co/data/.

Hello Inès François ,

I presume that you're talking about instances other than ignorable missing values. (An example of this would be a skip logic survey, such as: Q1: Are you married? If not, skip to question #4. Anyone answering "No" to Q1 shouldn't have responded to Q2 or Q3.)

If the sheer amount of missing data doesn't exceed 5%, you likely don't have to worry much about working with only complete cases. Some sources say that up to 10% can be tolerated.

However, it is important to check whether missingness on a variable is in any way related to other characteristics of a case, which could include demographic attributes, and/or the way a case responded to some other item. If it is, then you must be more cautious about how to proceed.

You can impute responses for categorical variables; it's just more easily done for continuous variables.

Finally, do note that a lot of times, cases with "no response" or "refusal to respond" are treated as if they had elected an otherwise viable option (hence, a three-category item could functionally be treated as a four-category item, with missing responses assigned to the fourth category). This is because numerous studies have shown that sometimes, these folks _are_ different in one or more important ways from those that did respond.

Good luck with your work.

Dear doctor

I quoted the following from web hoping being satisfying

"Traditionally, data engineers write data quality rules using SQL. This manual method works well when there are dozens, or even hundreds of tables, but not when there are ten thousand or more. In a modern, data-driven organization, data engineers can never keep up with demand for data quality scripts.

New data quality automation (DQA) tools replace manual methods with ML models. You can view this product segment as a subset of data observability, which addresses both data quality and data pipeline performance. There are three different approaches to DQA: automated checks, automated rules, and automated monitoring. Each approach has its pros and cons, but collectively they represent the future of data quality management.

The following three vendors embody these approaches, respectively.

Dr.Sundus Fadhil Hantoosh

The plot shows that the average quality per base alongside your 150 pb reads is very high which is good. This result is kind of normal when the sequencing has been outsourced. Most companies will give you prefiltered fastq files containing only reads with high quality. You can ask your sequencing provider if that was the case, although sometimes you can also find that info in the report they send together with your fastq files.

In case-control studies, you can do it in multiple ways. I assume you already know how to get the controls. Imagine, you are performing secondary analysis, you have a dataset with 200 patients, here you have 30 people of SLE (case), and 170 people without SLE (control), as we all know matching 1 case with '1 to 4' controls provides adequate power, don't match more than 4 controls for 1 case.

First, match 1:1, that is 1 control for 1 case. As sex is almost always binary, you don't have to manipulate this variable. But age is not binary, so you have to determine the age range you would go. For instance, a 20-year-old man could be matched with 18-25-year-old men in your dataset, this way you could get. Create a categorical variable for age by specifying that range. Also, create a binary variable of who are cases and who are controls. Then, export these 60 people from the dataset and can perform your analysis. If you want to get 1:2, the procedure is the same (export 90 people), but whatever you do, prespecify the hypothesis, sample size, and analysis plan in the beginning. Best of luck!

You can also see propensity score matching or inverse proportional treatment weighting but that procedure is a little complex and you need somebody with advanced statistical analysis knowledge to do that.

Hello,

For the general case of time series anomaly detection, several benchmarks have been recently proposed. These two benchmarks contain labeled time series from different domains (for instance, room occupancy detection from temperature, CO2, light, and humidity [1] or accelerometer sensors of a wearable assistant for Parkinson's disease patients [2]).

You may find the links to the benchmarks below. Good luck with your thesis!

- TSB-UAD: https://github.com/johnpaparrizos/TSB-UAD (paper: )

- TimeEval: https://github.com/HPI-Information-Systems/timeeval-evaluation-paper (paper: )

[1] Luis M. Candanedo and Véronique Feldheim. 2016. Accurate occupancy detection of an office room from light, temperature, humidity, and CO2 measurements using statistical learning models. Energy and Buildings 112 (2016), 28–39. https://doi.org/10.1016/j.enbuild.2015.11.071

[2] Marc Bachlin, Meir Plotnik, Daniel Roggen, Inbal Maidan, Jerey M. Hausdor, Nir Giladi, and Gerhard Troster. 2010. Wearable Assistant for Parkinson's' Disease Patients With the Freezing of Gait Symptom. IEEE Transactions on Information Technology in Biomedicine 14, 2 (2010), 436–446. https://doi.org/10.1109/TITB. 2009.2036165

You might try deleting the current product system and make new flows, processes and product system. Seems like some mistake has been made in previous steps.

hi,

quantitative techniques for evaluating GAN generator models are listed below.

for more info:

Best wishes..

Developing countries can make use of a lot of health research conducted in developed countries, for these researches are scientifically proven, while those of developing countries are of short data for the universities there do not pay enough to do excellent researches.Regards.

Dear Prof. Dr. Devaraj Acharya , several methods could be used to ensure qualitative research analysis, such as Guba and Lincoln’s Concepts for defining and investigating quality in qualitative research as the following RG link. Kindly visit.

Kind Regards,

Hi Alba.

I had experience with Novogene, both for whole-genome and metagenomics.

The sequencing is very good, and I had no problems at all in terms of quality.

I don´t recommend to subscribe for their bioinformatic analysis, since the analyses performed are pretty basic and can be done easily by the researcher, and many times you need more complex analysis that are not included in the price.

Let me know if you need more information.

Ricardo

Then in your case, I would do a comparative analysis of the variables. For example, I would build a scatterplot between variables on the reference data, and I would also build the same scatterplot between variables in the data under study.

I think you need to analyze the hydrology of the study area. At the same time, I think, there may be mistakes

This paper and its reference list should help:

Marsh, H. W., Martin, A. J., & Jackson, S. (2010). Introducing a short version of the physical self description questionnaire: new strategies, short-form evaluative criteria, and applications of factor analyses. Journal of Sport and Exercise Psychology, 32(4), 438-482.

Yes, you can make 10 ul reaction mixture for taqman! However, during running PCR you need to make sure that you have changed the the total volume to 10ul instead of 20ul.

Best of luck

Hi Johnson Masinde , thank you for your answer. I requested the file to have more details but it seems answering partially. I'm more interested in the data improvement methods in case of limited datasets.

ecological footprint is better, because it is comprehensive data and include Carbone footprint as I think.

Collection of real time and crisp data and its validation.

I have a few ideas. Try to find out who installed the equipment and collected the data, who interpreted and edited data for final data set entry. What were their methods and quality control. Do you have just digital data, or examples of strip charts, punch tapes, site visits with comments, etc.? Who worked up data, how did they deal with missing records? What equipment were used, was it installed correctly for research, etc.? Are there documents such as a data library, publications, pictures? Can you visit sites of data collection, and look over any remnants as staff gauges, benchmarks of cross sections or bridge sites, historical aerial photos covering area, streamflow records used for stage discharge curves, adjustments with time. Location of rainfall sites relative to watershed measured and topography. I think you would find in discussing this topic with the US Geology Service professionals and research hydrologists or others involved with this work, a substantial amount of time is used in development of a quality data set. Someone should have documented the specifics, but realizing how sometimes this is not done or personnel changes and may cleanout and dump files or records, even reports, much can unfortunately be lost with time. Some research grade stations like USFS Coweeta Hydrologic Experiment Station or agencies like USGS take much effort in cataloging, filing, logging and storing past records, notes, etc. for posterity.

Also, plot the time series data out. Do the data of the various stations seem to correlate with each other. Are the readings frequent as many times per day, which is a good sign, or infrequent as daily readings, not as good quality. Do the flow hydrographs coincide with rainfall events? Temp changes with day and night, rainfall events, etc. Are there other streamflow data stations in vicinity which have good correlation with records taking into account some time differences in storms and response.

Hi Juelu Wang , you can see the conductivity of your measurements on the Zeta Report or also listed in the Zeta workspace. Yes, it appears that you have enough scattering intensity signal. If your overall mean zeta potential is very close to zero then it may be difficult to improve the data quality. The diffusion barrier method may improve. Here are a few comments that may be useful:

on mean zeta: https://www.materials-talks.com/blog/2017/09/05/what-is-the-zeta-deviation/

on measuring in high salt (just for info): https://www.materials-talks.com/blog/2020/07/15/measuring-zeta-potential-in-saturated-ionic-strengths/

on diffusion barrier: https://www.materials-talks.com/blog/2015/11/24/diffusion-barrier-method-the-practical-details/

and finally, feel free to add a data file: https://www.materials-talks.com/blog/2014/08/19/faq-how-can-i-submit-a-data-file-to-the-help-desk/

Ms Breanne R. Helmers

Unfortunately, I found another recent study on the quality decrease of MTurk data: An MTurk Crisis? Shifts in Data Quality and the Impact on Study Results by Michael Chmielewski, Sarah C. Kucker.

https://journals.sagepub.com/doi/abs/10.1177/1948550619875149?journalCode=sppa

You can apply some basic clustering techniques i.e. k-means, DBSCAN and so on for the outlier detection and removal.

The low quality data may be noisy and untrained as you have written such data could be reprocessed but if data is irrelevant , out of context and not explained the problem and requirement then the data would be futile . The unsupervised method or rule based method could help by the understanding the new problem and the rules those would help to take decisions .

Excellent quality. I have run RNAseq, BSseq and genome sequencing (our own libraries and novogene-built libraries) though their facility and always get fantastic data.

Hadi Fadlallah , yes. That should decrease computational expenses, help to perform an investigation of a subset instead of the whole set. That is similar to a data science process where a small dataset is analyzed afterwards the methods are applied on big data set.

Sorry I'm late to the party but if you know which datum is faulty you should be able to solve the problem. If you don't know how do you decide?. Which came first the chicken or the egg?

David Booth

BTW outliers often get a bad rep but are actually useful.. How do you tell the outliers from the approx. 2.5 % of data that lie in a tail. see the attached

This is like saying a road grader is poor because it doesn't do well on a concrete pavement. It is like saying the fish tastes red, Red is not a property of fish tasting and machine learning is not a form of data.. WORDS HAVE MEANINGS. Thanks to Prof. Arnold Insel for the fish tasting example. David Booth

Collecting and filtering of huge amounts of data in a distributed system is a pretty challenging task I faced.

In the early stages, our team trained a basic ML model that had low accuracy, and the reason was - bad data. So, the most crucial part is EDA or Exploratory data analysis to investigate trends and get some knowledge from the data. Our team discovered which fields are more applicable than others and how we can use them to increase accuracy.

Also, it turned out that these factors were not unevenly distributed, for instance, Factor 1 has 99% presence in the data, whereas Factor 2 has only 1% of it, and the task was to predict Factor 2. So, we collected the data appropriately again, iterate through all the ML processes and managed to get much better accuracy (more than 90%).

Consequently, a dataset for machine learning / AI/data science ought to be carefully selected and processed. Its collection is more engineering than a research task but still has a lot of importance.

Dear Ruslan,

I think it is worth checking out the EasyqpcR (https://www.bioconductor.org/packages/release/bioc/html/EasyqpcR.html). It is is a very comprehensive and well documented package and has been used in a variety of most recent publications. Major advantage is that it can be run via a GUI (using gWidgets; see: https://bioc.ism.ac.jp/packages/release/bioc/vignettes/EasyqpcR/inst/doc/vignette_EasyqpcR.pdf) which makes it very easy to use.

If you are looking for a more advanced tool to analyze your qPCR data which can cope with raw intensity values and perform melting curve analysis etc. I recommend the qpcR package (https://academic.oup.com/bioinformatics/article/24/13/1549/238435) which can do quite a lot of cool stuff.

If you are running a high through put experimental test design maybe check out this tool: HTqPCR (https://academic.oup.com/bioinformatics/article/25/24/3325/235116)

To facilitate qPCR data import there is another nice tool available called ReadqPCR (https://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.html).

For more options check out the comprehensive review from 2014

about qPCR analysis tools which compares among others, 9 R packages. Maybe you will find something more interesting ;) Good luck!

Hello, similar topic discussed here. https://www.researchgate.net/post/Can_anyone_help_me_with_k_fold_cross_validation

Good luck!

Thank you all for the support!!

In some instances we use other softwares to perform the statistical analysis based on the syntax developed and report this in the write-up for data quality and assurance purposes.

Panagiotis Loukopoulos If you're starting with a dry powder you have a fused collection of sub- and post-micron aggregates and agglomerates. Have you measured the SSA of your starch? If so, what is it? The behavior of the material will reflect the bulk size in terms of properties such as flowability, dusting tendency, filter blockage etc. Look at Figure 2 in the attached. Nowhere do you see free, independent, discrete particles < 100 nm. Thus your measurements should be taken on laser diffraction and not DLS. Many starches are also in the 20 micron region for mean size and thus, again, not applicable to DLS. Takse a look at this recent webinar:

May 28th, 2019 Dispersion and nanotechnology

Your Co tube case apparently suffers from low photon flux at the detector (compared to the Cu tube case). Do you have taken the same sample?

It seems to me that you might have got an experimental issue here.

For example the sample height adjustment needs to be improved.

Please play around with the height position of the sample with respect to the x-ray beam (rotation axis of your set up).

Good luck

Sounds like you've created an instrument which is sound as it is based on theory - but this version should only be the pilot version, rather than the final instrument. As you see - there are too many items, which will affect the quality of the data collected. Have you conducted any sort of pilot study - factor analysis - and tested to see how those items relate? You'll probably find some items are redundant, and possibly even some scales ... use EFA to explore how the items load - then as you delete items - the number of items in each scale will decrease - you can then delete scales on the basis of insufficient items.

I find David De Vaus' work on survey design and validation very useful:

https://www.amazon.co.uk/Surveys-Social-Research-Today/dp/0415530180/ref=pd_lpo_sbs_14_t_0?_encoding=UTF8&psc=1&refRID=ER3KY0K00XZC84NB7GHW

Sampling is the basic part of data science . In statistical quality assurance few imperical formule are available of which variance is most important.

Thanks @David -

I take it that it is OK for me to use the data in Appendix 1? Would you happen to have an excel file of those data?

Thanks again - Jim

Political anything can have negative connotation to many people.

Ikhlas Almukahel ,

Thank you for your detailed answer. May I assume that you agree but there need to be more things for data quality assessment?

In addition of sources written by Kilian Vos there is a Big Data Platefore created by FAO to standarised big Maps produced from remote sensig :

Link ; http://www.openforis.org/tools/sepal.html

I think you should refer to standard number 1053. You can find the quality requirement for drinking water

Tea tasting is the process in which a trained taster determines the quality of a particular tea. Due to climatic conditions, topography, manufacturing process, and different clones of the Camellia sinensis plant (tea), the final product may have vastly differing flavours and appearance

The ISIMIP project has a lot data on sustainability:

* Fastq is the sequence + quality information. This file is basically provided by the sequencing company. It is used to control the quality of the reads going in further pipeline. Fastq file is converted to Fasta after adapter trimming and quality assessment.

* Fasta files are the sequence files which have sequence attached to a specific identifier. These are generated after the quality filtering, and size of file is smaller than fastq.

* Qual files are the Fastq minus fasta, this only has quality information and generated after spliting fastq file.

If you are using a pipeline which filters out good quality reads (your threshold), you don't have to think about what fasta and qual file has. Fasta files will become comprehensive after dereplication and clustering the reads into OTUs. Therefore, just follow the protocol. Meanwhile, please go through the manual of mothur to understand how the process works. I suppose you should do it before you start the actual analysis.

CMIN/df is not really used in these large sample studies, so we can safely ignore it. This leaves the outlier of the RMSEA. In my experience, simple models (1 or 2 factors and a low number of items) tend to follow the pattern in your results.

The main difference of RMSEA compared to the CFI (and other measures) is that RMSEA is a measure of absolute fit and does not have any corrections based on model compexity. It appears you may have a very simple model (2 df), although you don't specify here the exact model, so that assumtion be wrong.

Now, the SRMR is also an absolute measure of item fit, but you have a good SRMR. However, according to David A. Kenney (http://davidakenny.net/cm/fit.htm), the SRMR is biased in favor of low df, which you certainly have.

What does this mean? Well overall you have a simple model that captures a good amount of the variance considering how simple it is, but there's still a large absolute error. Do you need to alter you model? Well maybe. If there are clear theoretical reasons to do so, you should. However, considering all fit metrics, it is probably "acceptable," but not "good." So I wouldn't change the model unless there is a good theoretical rationale. Then you'll want to provide a good explanation of the fit metrics and any biases in any article or paper you write based on this model.

So is it a problem? Maybe. Depends on how complex your model really is and if there is a theoretical rationale to update your model.

Is it a problem of data quality? Maybe, but probably not. Most SEM techniques can adapt for variance, missing data and so on. It depends on the estimator you use and the degree of these issues. you don't say what estimator you are using or if the data are continuous, categorical or interval.

Is it because of the low df? Probably, in as much as you probably have a simple model and there are some inherrent biases in some of the measures.

No worries. You're welcome.

If the target labels are in your data, then you have a classification task (not clustering).

At the very early stage, make sure that your dataset is prepared well. Then, try many classification algorithms, where each of them needs to be evaluated with k-fold cross-validation, for instance.

You need sometimes to tweak the classifier based on the nature of the data, such as selecting the right kernel.

Cheers,

Dr. Samer Sarsam

No, unfortunately. I am still working on it. However, take a look at the study attached. In the opposite way, they consider a fixation accurate where the dominant eye is located on the target. Also, what eye tracker are you using? I am using the tobii x3-120 and in their manual they recommend using the dominant eye for more accurate precision. I am finding in my study the dominant eye is more accurate too.

Paterson, K. B., Jordan, T. R., & Kurtev, S. (2009). Binocular fixation disparity in single word displays. Journal of Experimental Psychology: Human Perception and Performance, 35(6), 1961-1968. doi:10.1037/a0016889

The index will be used for classification and for prediction (probably with KNN).

agree with @ Ajit kumar Roy

regards

There are many data analytic tools (usually commercial) that claim that they can provide information about the quality of your files (and some also claim that they can subsequently provide files where the errors are 'corrected'.) The methods are called 'profiling' tools. I am not aware of any that work in a minimally effective manner. Difficult errors are determining 'duplicates' in files using quasi-identifying information such as name, address, date-of-birith, etc. Two records may be duplicates (represent the same person or business) even when the quasi-identifying information as representational or typographical error. Any quantitative information from the two records representing the same entity may have slight (or major) differences. If the records having missing values associated with the data that an individual wishes to analyze, then the missing values should be filled-in with a principled method that preserves joint distributions (e.g., Little and Rubin book on missing data, 2002). The 'corrected' data may also need to satisfying edit constraints (such as a child under 16 cannot be married).

htpps://sites.google.com/site/dinaworkshop2015/invited-speakers

You can find useful tips on relevant papers and books, for example:

Hey Zhou,

I would add, that it critically depends on what you mean by "serious neuroscience & psychophysics experiments".In the linked question, I have collected some related questions to ET use.

In general, if you don't need subject mobility, more restraints are better (head/chin rest, bite bar, etc.) and will yield better results (in terms of accuracy and precision).

In addition, stationary ETs are currently still faster by a large margin. The fastest VOG-based head-mounted ET is around 300 Hz whereas for stationary, head-fixed VOG-ETs you can got up to 2000 Hz. Since head-mounted ETs use a (low frame rate) scene camera to record the participants environment, many systems don't see the need to go to higher sampling rates.

For fixation analyses, which is more common in psychology & related fields, low sampling rates are not a problem.

But as soon as you go for saccades, sampling rate becomes a critical factor.:

Finally, the calibration makes a difference (see Nystroem2013).

TL;DR: Provide more info on what you plan to do. If you analyses saccade peak velocities, get a stationary ET.

Hope that helps, Greetings, David

There are many tools actually. If you find my book chapter useful, it could help you hopefully. Although title says it's basically about RNA-Seq data but you would get idea about many tools that apply to NGS data QC. Below is the link of my chapter.

The RedCap administrator is able to provide access to double entry of data under user rights for your project. You can further define if one or two users will capture the data. At our university it is fairly easy to get the RedCap administrator to make changes to a project.

All the best wit your RedCap project.

Ref:

Dear Christoph Kowalski, it is not exactly what you are asking for (i.e., a review or study reporting on the increased acceptance or satisfaction with the reporting system) but 'timelines' and 'punctuality' in the provision of analytical data is considered as a quality criteria for any monitoring system (even more so in the case of clinical performance analysis systems). 

As an example, the Eurostat Quality Assurance Framework establish both 'timeliness' and 'punctuality in the delivery of metrics' as their 13 principle (page 30, http://ec.europa.eu/eurostat/documents/64157/4372717/Eurostat-QAF-V1-2.pdf) defining 'timeliness' as 'the availability of the information needed to take action at the appropriate moment of decision making', meaning that there has not necessary to be from a precise period, but be ready and available to the professional (i.e., health or otherwise) to facilitate an informed decision. In clinical settings, is established depending on contextual needs. One of the best examples you could use to argue your point is to follow the analogy of the laboratory test in the emergency care as an information system allowing timely decision making based on current data on the patient, thus promoting action to change an urgent situation. This example can be extended to other health information systems already mentioned in the other answers above. 

Similarly, 'punctuality' to deliver those metrics following a predefined delivery plan is also stated as a data quality principle as it sets the expectative of the user of the information system on the data availability within acceptable margins, facilitating the use of the information provided.

Both 'timeliness' and 'punctuality' are quality dimensions that you should take into account when trying to create an information system to be useful in the clinical context. 

don't know.  First I'd want to see the data, and then see what makes sense....  however, since I haven't seen anything... no worries

thanks for replying,

best regards,

joe

It seems useful:

You should always conduct a pilot study / test before going for "big bang" data collection.  The purpose of the pilot study include the following which is not an exhaustive list:

Dear prof. Gorodetsky,

I'd suggest you to analyze the existing literature on your "ill-structured" domain by means of topic modeling techniques. By inferring relationships across topics you can build the dependency model you are after. In the attached paper you can find a methodology we have designed in order to perform such an analysis.

Regards,

Andrea De Mauro

I am afraid that a simple Z - sizer is not an appropriate instrument for such measurements. To be sure in a result of the measurements, they  should be done at 2 or 3 scattering angles and it should be checked that the correlation function processing program allows a repetition of the location of size distribution peaks. We should also note that when particles are large (and their amount is small) in the different times the scattering volume can be provided by different particles. Therefore at each scattering angle few measurements are needed. So for DLS measurements of complex size distribution you must use Malvern DLS sizer with opportunity for angle changing or analogous Nanotrec sizer or Photocor FCN sizer with good program for correlation function expantion on exponents. For Photocor the program DynaLS is sufficient, for other sizers compatible software is needed. Look for the DLS installation of this class.

Yes, I think SEM should help. But a model should enumerate indigenous and exogenous variables with well grounded hyptheses and should not have either type-I and/or type-II errors.

But, it would be necessary to validate with case based inputs.