Data Curation - Science topic

I think this link will be helpful.

Dear Ahmad,

If there is some data, there is some spreadsheet that explains those data (I'm talking from experience, that happened to me once, I spent hours finding that paper but it existed nonetheless).

Now, without looking at that spreadsheet, I can suppose it's the *sum of the masses*; because we cannot measure the mass of a single star, but we can measure the sum of gravitating masses via Kepler's third law [T²~d³ and the constant of proportionality contains the sum].

Hope that this helps

Coefficient alpha is based on correlations, so you be having a problem with missing data for "complete" cases. But you should also check to see if your problem is structural in the sense that you are including items from two (or more) different surveys in the same analysis, because it would be impossible to calculate correlations when the variables are drawn from different data sets.

For the higher abundance, lower variation oxide, I might suggest Al2O3. Lower abundance oxides or elements have the problem of lower analytical precision in general as well as having precision which can vary greatly from lab to lab. Al2O3 also tends to be less impacted by beam damage effects or glass alteration both of which make the alkalis a poor choice.

On the problem of zeros and unreported data, this is a substantial data reporting issue and a misuse of detection limits. When the glass composition of a tephra is analyzed, there are almost always multiple analyses, and these are used to produce population summary statistics like means and standard deviations. This and the inherent randomness in X-ray counting are important to keep in mind when considering what to do when specific analyzed values on specific individual shards fall near or below detection limits. Consider what would happen to the mean and standard deviation of a population of analyses if one threw out half of a set of analyzed values because they fell below the detection limit and kept only those above. The result would completely misrepresent the nature of the distribution. I argued in an open review at Geochronology that all of the analyzed values must be reported always, and even negative concentrations must be reported! Again, this is necessary to represent the nature of the statistical distribution, to not skew the mean toward higher concentrations, and to provide an appropriate representation of the precision. More explanation here: https://gchron.copernicus.org/preprints/gchron-2020-34/gchron-2020-34-RC3.pdf

New Model for the COVID-19 Reported Cases and Deaths of Ghana in Accelerated Spread and Prediction of the Delayed Phase

You deserve it well, well done. make it smooth and visible to read.

BioTuring Browser can be helpful. It is an interactive platform for access and reanalyzing published single-cell RNA sequencing data.

Exactly. However, more planning is needed from now own. Thank you so much for your enlightening and innovative thoughts

I have not lost the data of any ongoing research project, because in parallel I work at home and do independent archiving. Experiments have been suspended, which is a waste of time.

No, there is nothing precisely like that.

"Random" is what we can not explain or predict (for whatever reason; it does not matter if there is no such possible explanation or if we are just not aware of one).

The model uses some predictors (known to us; like the time of the day, the wether conditions including the day in the year, etc.) and makes a prediction of the response (the energy consumption) - the response value we should expect, given the corresponding values of the predictors. You can see the model as a mathematical formula of the predictor values. The formula contains parameters that make the model flexible and adjustable to observed data (think of an intercept and a slope of a regression line, or the frequency and amplitude of a sinusoidal wave).

The deviation of observations from these expected values are called residuals. They are not explained by the model and are thus considered "random". This randomness is mathematically handled by a probability distribution: we don't say that a particular resudual will be this or that large; instead we give a probability distribution (more correctly, we give the probability distribution of the response, conditional on the predictors). Using this probability model allows us to find the probability of the observed data (what is called the likelihood) given any combination of chosen values of the model parameters. Usually, we "fit" these parameters to maximize this likelihood (-> maximum likelihood estimates).

Thus, given a fitted model (on a given set of observations), we have a (maximized) likelihood (which depends on the data and on the functional model and on the probability model).

This can be used to compare different models. One might just see which of the models has the largest (maximized) likelihood. There are a few practical problems, because models with more parameters can get higher likelihood s just because they are more flexible - not more "correct". This is tried to be accounted for in by giving penalties for the model flexibility. This leads to the formulation of different information criteria (AIC, BIC, DIC and alikes, that all differ in the way the penalties are counted).

So, after that long post, you may look for such ICs to compare different models. The limitation remains that the models are all compared only on the data that was used to fit them, without guarantee that they will behave similar for new data. So if you have enough data it might be wise to fit the models using only a subset of the available data and then check how well these models predict the rest of the data. It does not really matter how you quantify this; I would plot the differences of the models side-by-side in a boxplot or a scatterplot.

Hi Ana, you should try the bibliometrix package in R ( https://bibliometrix.org/ ). Even if you don´t know anything about programming there is a web-based application (biblioshiny). With this package, you can analyze everything you want by drag and drop the items you want to investigate.

Im not sure whats your aim from this reading but why you don't try pressure sensors

Transforming this by entering interesting stories that urge learning through exploration and stimulating the motivation of learners in research. In addition, there can be scientific questions in the games that need an answer to move from one stage to another stage, thus making the learner search for the answer to the question

Hi Melissa. I don'. Do you mean private opinion surveys ? They just ask who the interviewee voted for or who they intend to vote for. If I misunderstood your question and it doesn't have anything to do with my answer, I'm really sorry.

Hello David

Thank you vor your response.

In fact, I compared the data wiht and without the 8 excluded cases.

The differences are rather small, and all correlations for valence and for arousal between the whole sample and the 'reduced' sample are >.99 for all rating groups.

Best regards

Egon

I suppose it all depends on your study species. For the most part, I think experts in most fields are able to identify the species they're most knowledgeable about with relatively high accuracy, given they have enough information in the photo and geographic location to do so.

It's usually when someone gets a bit overzealous and identifies something to the species level when given minimal information and just going off of an educated guess for species most likely to be in the area.

It also depends on what the question for your study is. If you're doing an SDM for a species, you could always thin the records to about 100 and then self-verify (if you're confident in your abilities to do so). You could see if the species occurrence data has any corresponding NCBI molecular data and use DNA to verify species.

If you're using a dataset of 1000 + (or some other number where it isn't feasible to self verify each account) from inaturalist, you could query the data with >3 verified ID agreements with no "maverick" or disputed IDs. The likelihood of obtaining false positives should decrease with user agreement on a species identification.

Hey there,

probably have a look at biomaRt ( https://bioconductor.org/packages/release/bioc/html/biomaRt.html ) or online : https://www.ensembl.org/biomart/martview/d9adba17c8fe12b42ec108e61bae26fe

Here you can easily convert different naming.

Hope this helps.

yes I hope obtain scholarship to study PhD

You can simply use the `subset' command to select a part of the full data set with certain conditions.

dfagg1<-subset(dfsub, !is.na(id1) || !is.na(id2))

It will create a data excluding those subjects for whom id1 and id2 both are missing.

SMARTS is how I'd address the problem, especially since you seem to want a custom-list of what counts as inorganic. That makes it very unlikely you'll find a pre-made filter that will give you the output that you want. That being said, I'd probably start by looking for absence of carbon, then do a second pass looking for the presence of metals. There are a lot of metals, but it's probably simpler to type those in than to try and imagine every possible organo-metallic ligand under the sun. If there is carbon, and is not any metal, chances are pretty good you have an organic compound.

As for docking algorithms (or, more specifically, scoring algorithms), it's true that they mostly handle inorganic compounds with some degree of success these days. But the exact quality of the output will depend upon the input: some inorganics will likely be handled better than others, just like some organic compounds are more reliably modeled by any given algorithm than others. Inorganics have the further problem that there are far fewer inorganic/protein co-crystal structures out there, so it's harder to benchmark an algorithm based on that sort of data.

At the end of the day, I suppose the big question is: what do you want? What sort of output are you looking for? If you're just doing high throughput virtual screening, I'd almost say to skip the filter at the start - see what your output looks like, and just mentally classify results as more or less reliable based on what the molecules look like, and whether they are organic or inorganic.

Ingrid Del Valle García Carreño thanks for your comment ingrid.

I added a specific example for a workshop I'm working on here. I hope that would make it clearer.

email response from Paul Bradshaw (Birmingham uni) of onlinejournalismblog.com: " Hi Hashem, this is quite a broad question and so the answer is hard to give. There are many ways to find statistics/figures – for example using advanced search operators like site:ac.uk or filetype:xlsx will help you narrow your search, or specialist search engines like Zanran (which you mention) which focus on tables and charts. You can also try a two-step approach by first searching for organisations which are likely to collect that information (governmental, charities, academic, survey companies) and then approaching them. More broadly you can look at a system and identify which organisations collect which information within that system (e.g. Regulators do inspections, using gov.uk etc) In the specific example of career deliberation you may be best trying the two-step approach. It may be that recruitment companies have commissioned research of this type before, or you can access polling companies to find out if they’ve done anything on this.

Thanks,

Paul Bradshaw"

Two distinct problems need to be addressed here. First, an inevitable experimental error (sometimes very significant) occurs when measuring a specific biological effect. In this case, it would be highly recommended to analyze as many data sources as possible to select the most reliable one or to combine multiple data sets by averaging data for the same ligand or otherwise. Second, different assays may measure different phenomena, e.g., binding to a receptor vs. an antagonistic effect on the same receptor. Here, mixing the data would not make any sense. Two different QSAR models should be developed for respectively binding and antagonism.

Thank you Shen-An. I am in the process of educating myself, reading published literature. Tedious, but necessary!

Dear Eduardo Goncalves Paterson Fox

now I see your point.

However scientific data repositories are designed to store raw data. They guarantee durability, versioning etc. but not manipulating that data. Even the preview functions of PDF or JPG are only additional features and every PDF reader will do it better. So the intention of such repositories is to store raw data that everybody can download and manipulate it locally in its own way.

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