Sentences with phrase «in dataset»

Apartments in the dataset are professionally managed and tend to be of institutional quality.

(Looking at the table at the bottom of the page, it's seems 5 % to 6 % negative equity is «normal» in this dataset for whatever reason.

This resulted in a dataset of 1102 (443 boys, 659 girls) children and adolescents (Mage = 13.34, SDage = 2.54, rangeage = 8 — 18).

Second, missing values in the dataset were estimated with the Expectation - Maximization algorithm (EM).

Factors that should be taken into account in determining which datasets should be linked for enhancement purposes include: previous validations studies, representativeness of the community, and the extent to which information in each dataset is collected independently.

Not all of the outliers present in the dataset serve as money.

Each row in the dataset contains a nested record of user experience for a particular origin, split by key dimensions.

TensorFlow on a simple machine learning, once I found somebody who was offering up a dataset that I actually could play with a little bit, then that sort of seems — I don't know whether I will get the time to do it or that sort of thing, but at least it makes it doable, because I can say, oh, I can see a project, because given what's in that dataset, I can think of something that I could try to pull out of that and then in that way learn it.

Perform early case assessment through a broad set of analytic capabilities that enable rapid identification of the most important documents in a dataset prior to collection.

Unlike other passport indexes, which only count countries as destinations, the Henley Passport Index includes both countries and territories in its dataset, making for a more robust and holistic view of global passport power.

107 Figure 2, which is based on Models 2 and 3 in Table 4, was derived similarly to Figure 1 by setting continuous variables to their mean values, dichotomous variables to their modal levels, and ordered variables to their median values, as well as varying the readability score variable from -3 to 3, which is the approximate range of values in our dataset.

The last claim in our dataset is case 50 filed in 2006.

The last application in our dataset is case 535, filed in 2006.

I used the same data used by Steve Goddard, starting in 1971 to avoid early gaps in the dataset.

«For instance, one of us (Eric) feels more strongly that some of Prall's classifications in his dataset cross a line (for more on Eric's view, see his comments at Dotearth).»

Hu, I'm reminded of a method that I invented a while back to determine the amount of signal in a dataset.

This occurs on individual days of very low diurnal temperature range, at individual locations, and affects less than 0.03 % of the records in the dataset.

In fact, almost all of the ACORN - SAT locations in the dataset have sites that moved at least once in their history, and hence there is no continuous «raw» temperature time series available for these locations.

The lowest and second lowest temperatures in the dataset occurred with the highest and third highest proportion of males born (1992 and 1976, respectively).

Notes: Excel was used to calculate and plot the moving sea level per century curves and fitted trends (Excel slope function produced trends based on moving 360 - month periods for each month in the dataset; then converted to per century trends (inches) for each month).

There is a recognised bias in the dataset from the period around WWII associated with changes in the nationality of the shipping fleets taking sea surface temperature measurements - the main contributor to the temperature record - due to the war.

It is also important to note that the changes in satellite viewing do not occur slowly; rather, they occur as jumps in the dataset (i.e., near instantaneous changes to a new mean value).

In our dataset the value of the reference modulation potential (see Eq.

Thus, the same key pattern of low - frequency variability in the dataset should have been retained as an indicator.

Seems that, in this dataset, the periods around 1450, 1525 and 1825 were quite cold (statistically significantly different from zero).

We all know that one way to discredit a dataset (in this case the Leroy (2010) site classifications) is to find data in that dataset that is clearly incorrect.

It looks like there may be some interesting info in this dataset but I see no point doing any further d.p. unless I have the true data.

Linear regression determines the underlying trend in a dataset over a given period as the slope of the unique straight line through the data that minimizes the sum of the squares of the absolute differences or «residuals» between the data - points corresponding to each time interval in the data and on the trend - line.

On any timespan in the dataset, if you choose short enough samples, you will produce many, many trendlines of contradictory sign and rate.

Likewise, homogenization is meant to adjust for changes and biases in station data, resulting in a dataset useful for climate research.

«Obviously» this had to be due to wealth creating the warming in the dataset, rather than any climate change — his conclusion.

They found that, no matter what choices they made in dataset construction, their bottom - line finding - that the surface of our planet is warming - was rock solid.

The three major groups calculating the average surface temperature of the earth (land and ocean combined) all are currently indicating that 2014 will likely nudge out 2010 (by a couple hundredths of a degree Celsius) to become the warmest year in each dataset (which begin in mid-to-late 1800s).

Copernicus Climate Change Service (C3S), operated by the European Centre for Medium - range Weather Forecasts (ECMWF), calculated the global average August temperature was nearly two - tenths of a degree Celsius higher than the previous August temperature records set in 2015, in their dataset dating to 1979.

Robust Z - scores were used to identify possible outliers in the dataset, as such values could distort the mean and make the conclusions of a study less accurate or even incorrect.

The final temperature in any dataset has a middle figure that's commonly cited and then a margin of error on either side.

Then make sure that you apply the final QA flags in the dataset.

So we reported on many problems in the dataset.

For the facilities in this dataset in the 2012 - 2104 compliance period there has been a 36 million ton reduction from the 127 million ton baseline or a 28 % reduction.

The justification for this approach is that the CO2 forcing data, since it has no high frequency content at all, can not match the high frequency content in the temperature series, and therefore should be fitted only to the very low frequency trajectory in the dataset.

This was due largely to a change in the dataset used to estimate Chinese emissions.

Using probability distributions of convergence / divergence and of rainfall intensity, thresholds were identified and used to classify extreme and background events in each dataset.

This program is described by Menne & Williams, 2009 (Open access) and adjusts each of the weather station records in their dataset so that it better matches those of its neighbours.

I know that actual evapotranspiration, potential evaporation, and potential evapotranspiration are often distinguised in the literature, so I also wanted to confirm what the potential evaporation rate in this dataset represents.

It is true that nearly one third of the stations in the dataset are still rural (1987 out of the 6051 non-U.S. stations).

However, for now, it is sufficient to note that almost all of the station records in this dataset are fairly long and complete (urban and rural).

We excluded the ten countries (Cook Islands, Federated States of Micronesia, Marshall Islands, Montenegro, Nauru, Niue, Saint Kitts & Nevis, Serbia, Somalia and Taiwan) with data missing in any dataset, and 179 remained for analysis.

Valentia Observatory also happens to be one of the longest and most complete rural station records in the dataset.

In my dataset (obtained from the obvious sources on internet) there is no 6 - 9 month time lag (not positive nor negative) between temperature evolution and CO2 concentration differences.

The paper says it used a «diverse multiproxy network» documented in Dataset S1 of the Materials and Methods supplement associated with the paper.