cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22082024
title: Calculation of Earth's atmospheric infrared radiation transmittance rate

description: 1. Excel is used to calculate the atmospheric transmission rate of infrared radiation. The results are presented in the cell “N10” of the two worksheets. 2. Cell “B7” in the worksheet corresponds to the Earth’s surface temperature, the initial value of which is 288K. If the temperature is changed to 210K and 350K, that is, the value in B7 is changed to 210 and 350, the results would be 14.99% and 28.57% in N10, which is basically the same as the original 15-30%, indicating that the data is well restored according to the figures, and the calculation result of transmittance is correct. 3. There are three Plantwo of which are functions of corresponding wavelengths, and the relatick laws, onship between the two functions is a constant. Transmittance rate is a ratio, and both the numerator and denominator can present the constant outside the sum function for approximate division, so there is no difference between the results of choosing either of the two functions. 4. The restoring process of the figure of “Radiation Transmitted by the Atmosphere” is as follows: a. Grid the picture; b. Obtain the function of the number of grids corresponding to the horizontal coordinate; c. Calculate the corresponding wavelength at the step length of 0.1 (the result is displayed in column D); d. Find the 1.5, 1.6 ......, 13.9, 14 μm in the wavelength, and check the corresponding number of horizontal coordinate squares n; e. Use the number of squares n in the “Total Absorption and Scattering” to find the corresponding atmospheric absorption rate r for that wavelength; f. Organize the data and list the 1.5 to 14 μm at the step length of 0.1 μm, and list the corresponding absorption rate r; g. Find the atmospheric transmission rate 1-r for wavelengths from 1.3 to 14 μm at the step length of 0.1 μm. Then the calculation would be easier. 5. Main radiation bands of CO2: 2.68-2.8 μm, 4.25-4.45 μm, 13-17 μm. Main radiation bands of H2O: 2.55-2.84 μm, 5.6-7.6 μm, 12-30 μm, all in the “Total Absorption and Scattering” atmospheric absorption range of infrared radiation. The calculation of transmission rate is not affected by the infrared radiation of CO2 and H2O. <br>
DOI: 10.6084/m9.figshare.22082024.v2
title: Calculation of Earth's atmospheric infrared radiation transmittance rate

description: 1. Excel is used to calculate the atmospheric transmission rate of infrared radiation. The results are presented in the cell “N10” of the two worksheets. 2. Cell “B7” in the worksheet corresponds to the Earth’s surface temperature, the initial value of which is 288K. If the temperature is changed to 210K and 350K, that is, the value in B7 is changed to 210 and 350, the results would be 14.99% and 28.57% in N10, which is basically the same as the original 15-30%, indicating that the data is well restored according to the figures, and the calculation result of transmittance is correct. 3. There are three Plantwo of which are functions of corresponding wavelengths, and the relatick laws, onship between the two functions is a constant. Transmittance rate is a ratio, and both the numerator and denominator can present the constant outside the sum function for approximate division, so there is no difference between the results of choosing either of the two functions. 4. The restoring process of the figure of “Radiation Transmitted by the Atmosphere” is as follows: a. Grid the picture; b. Obtain the function of the number of grids corresponding to the horizontal coordinate; c. Calculate the corresponding wavelength at the step length of 0.1 (the result is displayed in column D); d. Find the 1.5, 1.6 ......, 13.9, 14 μm in the wavelength, and check the corresponding number of horizontal coordinate squares n; e. Use the number of squares n in the “Total Absorption and Scattering” to find the corresponding atmospheric absorption rate r for that wavelength; f. Organize the data and list the 1.5 to 14 μm at the step length of 0.1 μm, and list the corresponding absorption rate r; g. Find the atmospheric transmission rate 1-r for wavelengths from 1.3 to 14 μm at the step length of 0.1 μm. Then the calculation would be easier. 5. Main radiation bands of CO2: 2.68-2.8 μm, 4.25-4.45 μm, 13-17 μm. Main radiation bands of H2O: 2.55-2.84 μm, 5.6-7.6 μm, 12-30 μm, all in the “Total Absorption and Scattering” atmospheric absorption range of infrared radiation. The calculation of transmission rate is not affected by the infrared radiation of CO2 and H2O. <br>
DOI: 10.6084/m9.figshare.22082015.v2
title: The absorptivity and transmittance of infrared radiation per unit wavelength corresponding to the earth's atmosphere

description: When calculating the atmospheric absorptivity corresponding to the wavelength, first grid the picture of Radiation Transmitted by the Atmoshpere(https://doi.org/10.6084/m9.figshare.22081277.v3). In this case, the horizontal axis coordinate is an exponential function, and the function expression is a function with a base of 10 and an index of (n-26)/37; find the corresponding n value through the wavelength, and find the atmospheric absorbance corresponding to the unit wavelength in the figure. Then get the dataset.
DOI: 10.5281/zenodo.7663324
title: SceneFake

description: Many datasets have been designed to further the development of fake audio detection. However, fake utterances in previous datasets are mostly generated by altering timbre, prosody, linguistic content or channel noise of original audio. These datasets leave out a scenario, in which the acoustic scene of an original audio is manipulated with a forged one. It will pose a major threat to our society if some people misuse the manipulated audio with malicious purpose. Therefore, this motivates us to fill in the gap. This paper proposes such a dataset for scene fake audio detection named SceneFake, where a manipulated audio is generated by only tampering with the acoustic scene of an real utterance by using speech enhancement technologies. The results show that scene fake utterances cannot be detected reliably by the baseline models trained using the ASVspoof 2019 dataset. When the models are trained using the training set of SceneFake, they perform well when evaluated with the seen testing set, but still perform poorly when dealing with the unseen test set. The SceneFake dataset is publicly available. The source code of baselines is available on GitHub https://github.com/ADDchallenge/SceneFake This data set is licensed with a CC BY-NC-ND 4.0 license.
DOI: 10.5281/zenodo.7663323
title: SceneFake

description: Many datasets have been designed to further the development of fake audio detection. However, fake utterances in previous datasets are mostly generated by altering timbre, prosody, linguistic content or channel noise of original audio. These datasets leave out a scenario, in which the acoustic scene of an original audio is manipulated with a forged one. It will pose a major threat to our society if some people misuse the manipulated audio with malicious purpose. Therefore, this motivates us to fill in the gap. This paper proposes such a dataset for scene fake audio detection named SceneFake, where a manipulated audio is generated by only tampering with the acoustic scene of an real utterance by using speech enhancement technologies. The results show that scene fake utterances cannot be detected reliably by the baseline models trained using the ASVspoof 2019 dataset. When the models are trained using the training set of SceneFake, they perform well when evaluated with the seen testing set, but still perform poorly when dealing with the unseen test set. The SceneFake dataset is publicly available. The source code of baselines is available on GitHub https://github.com/ADDchallenge/SceneFake This data set is licensed with a CC BY-NC-ND 4.0 license.
DOI: 10.17632/vjjd87vyrc.1
title: SNPs information

description: To evaluate whether psoriasis have an effect on the risk of developing NAFLD.Two-sample mendelian randomization study using genetic variants associated with psoriasis as instrumental variables to improve inference. Summary association results from large consortia of candidate gene or genome-wide association studies, including several epidemiologic studies with different designs, were used. Gene-psoriasis biomarker associations were estimated in more than 210,000 controls, while gene-NAFLD associations were estimated in more than 75,000 controls. Results revealed that total psoriasis and psoriatic arthritis were significantly associated with an increased risk of NAFLD. But psoriasis vulgaris was not significantly associated with an increased risk of NAFLD.NAFLD was also not significantly associated with an increased risk of total psoriasis, psoriatic arthritis and psoriasis vulgaris.This data is all SNPs information of the study.
DOI: 10.17632/bjstjh7v3s.1
title: IRAND-SimML

description: The dataset for ML is made up of muon and IBD events. The transport of about 16×107 optical photons led to the creation of this data collection. The dataset contains 16,796 different events  as "dataset.csv".
DOI: 10.17632/ct69znnt5j.1
title: Psychosocial factors influencing oyster mushroom commerciaisation in Ghana

description: Psychosocial factors are critical in spurring agricultural commercialization. The oyster mushroom is beneficial to Ghana's socio-economic development and has received attention from both the government and development partners to increase commercialization. While socioeconomic and demographic characteristics influencing agricultural commercialization have received a huge a scholarly attention, little is known, however, about the psychosocial factors that drive agricultural commercialization in general, and oyster mushroom commercialization in particular. The data was collected to be used to investigate the psychosocial factors such as motivations and impediments that influence oyster mushroom commercialization in Ghana, with emphasis on the Ga East and Adentan Municipalities in the Greater Accra region of Ghana.
DOI: 10.17632/ztfpdc5skg.1
title: Estimation of aboveground biomass and carbon stocks of tree saplings using UAV-derived RGB imagery

description: Database with UAV-derived tree variables and field tree variables.
DOI: 10.17632/zhcyyxwpr6.1
title: Work-Related Depression and Life Satis-faction: Moderating Effect of Job Guar-antee and Psychological Capital

description: The data set explores with the correlation analysis the relationship between depression and life satisfaction. It also investigates the moderating role of psychological capital. Depression is the IV, life satisfaction is the DV, and psychological capital is the moderating variable.
DOI: 10.17632/v8p7r5nfrf.3
title: Predicting forest products price trend: the case of Scots pine in Catalonia

description: The assumed rate of real price change, with the discount rate, are the critical factors in forest investment analysis. In addition, the rate of real price change allows us to plot a trend line based on time series reflecting existing or past market price, that is on "facts".
Usually, many potential users are not going to use sophisticated forecasting techniques to estimate future prices, preferring to rely on simple approximation techniques. If acceptable time price series is available, then the simplest approach is to evidence a trend line over time that can be extended into the future. This can be done with regression analysis. In working with historical data, we could arrive at a long-term trend estimate, which excludes the effects of inflation.
Although the real price of forest products does not usually vary in an exponential way, the normal practice in investment analyses is often simplified by compounding price using a real price change rate.
We can get the annual rate of real price change (r) from a linearized model that allow us to keep the statistical robustness of a linear regression model (with statistics, confidence indicators and tests), but applying the compound rate approach used in mathematics of finance. To do that, the well-known basic formula for compounding Pn=P0 (1+r)^n, where:
   Pn = estimated price in year n
   P0= price in year 0
   r = annual rate of real price change, that is real compound rate
   n = number of years from year 0

is transformed into that of a straight line by making a change of variables (linearization).

The proposed method is easy to reproduce and seems more orthodox than apply projections made using a straight-line model. Even though the straight-line represents an average variation over the years, from a mathematics of finance approach we should discuss price variation in terms of the annual compound rate. In Figure 1, you can see the differences between these approaches.
If we have a clear trend in past real prices and the likelihood of a real price variation, we could make future price assumptions. If you agree with this statement and believe that price trend based on historical patterns is a significative information, then you should use r value gotten from the linearized model here proposed to project the price according to the previous compounding equation, where P0 is any real price calculated through the linearized compounding model (Table 1).
In Catalonia, most of forest products prices have not kept up with inflation and reflect a declining trend. A few others have just barely kept up with inflation. This is means that, despite moderate growth in nominal terms, the real price of almost all Catalan forest products presents a negative trend. For example, Scots pine sawlogs -the most representative harvested species in Catalonia (the 27% of the total volume yearly logged)- have dropped by an average of almost 2% per year since 1980.
DOI: 10.17632/v8p7r5nfrf.1
title: How to predict forest products price

description: Although the real price of forest products is not used to vary in an exponential way, i.e. at a compound rate, the normal practice in investment analyses is often simplified by compounding price using a real price increase rate r. This seems more orthodox than apply projections made using a straight-line model. In Figure 1, you can see the differences between these two approaches. Even though the straight-line represents an average variation over the years, from a mathematics of finance approach we should discuss price variation in terms of the annual compound rate. This is the rate which, if compounded over the entire period, provides the expected price in the future. It is true that compounding, because of an exponential function, may give sharp price variation.
If you believe that price will continue to increase based on historical patterns, then you should use r figure got from the linearized model here proposed to project the price according to the following equation:
P_n=P_0 (〖1+r)〗^n
Where P0 is any real price calculated through the linearized compounding model (Table 1); r is the compound rate calculates in Table 1; the exponent n is the number of years the price is compounded.
In Catalonia, most of forest product prices have not kept up with inflation and reflect a decline and others have just barely kept up with inflation.
This is means that, despite moderate growth in nominal terms, the real price of almost all Catalan forest products presents a negative trend. For example, Scots pine wood has dropped by an average of almost 2% per year since 1980.
DOI: 10.17632/v8p7r5nfrf.2
title: Predicting forest products price trend: the case of Scots pine in Catalonia

description: The assumed rate of real price change is a critical factor in forest investment analysis. The real price change rate allows us too to plot a trend line based on time series reflecting existing or past market price, that is on "facts".
Usually, many potential users are not going to use sophisticated forecasting techniques to estimate future prices, preferring to rely on simple approximation techniques. If acceptable time price series is available, then the simplest approach is to evidence a trend line over time that can be extended into the future. This can be done with regression analysis. In working with historical data, we could arrive at a long-term trend estimate, which excludes the effects of inflation.
Although the real price of forest products does not usually vary in an exponential way, the normal practice in investment analyses is often simplified by compounding price using a real price change rate. This method is easy to reproduce and more orthodox than apply projections made using a straight-line model.
In Figure 1, you can see the differences between these two approaches. Even though the straight-line represents an average variation over the years, from a mathematics of finance approach we should discuss price variation in terms of the annual compound rate. This is the rate which, if compounded over the entire period, provides the expected price in the future.
The annual rate of real price change (r) is gotten from a linearized model that allow us to keep the statistical robustness of a linear regression model (with statistics, confidence indicators and tests), but applying the compound rate approach used in mathematics of finance. To do that, the well-known basic formula for compounding Pn=P0 (1+r)^n
Pn = estimated price in year n
P0= price in year 0
r = annual rate of real price change
n = number of years from year 0
is transformed into that of a straight line by making a change of variables (linearization).

If we have a clear trend in past real prices and the likelihood of a real price variation, we could make future price assumptions. If you agree with this statement and believe that price trend based on historical patterns is a significative information, then you should use r value gotten from the linearized model here proposed to project the price according to the previous compounding equation, where P0 is any real price calculated through the linearized compounding model (Table 1).
In Catalonia, most of forest products prices have not kept up with inflation and reflect a declining trend. A few others have just barely kept up with inflation. This is means that, despite moderate growth in nominal terms, the real price of almost all Catalan forest products presents a negative trend. For example, Scots pine sawlogs -the most representative harvested species in Catalonia (the 27% of the total volume yearly logged)- have dropped by an average of almost 2% per year since 1980.
DOI: 10.17632/v8p7r5nfrf.4
title: Predicting forest products price trend: the example of Scots pine in Catalonia

description: When deciding on how to estimate future prices, due to influences that are likely to affect a product, we should consider two factors: the expected inflation and the real price change.
The rate of real price change allows us to plot a trend line based on time series reflecting existing or past market price, that is, on "facts".
Usually, many potential users are not going to use sophisticated forecasting techniques to estimate future prices, preferring to rely on simple approximation techniques. If acceptable time price series is available, then the simplest approach is to evidence a trend line over time that can be extended into the future. This can be done with regression analysis. In working with historical data, we could arrive at a medium-term trend estimate, which excludes the effects of inflation.
Although the real price of forest products does not usually vary in an exponential way, the normal practice in investment analyses is often simplified by compounding price using a real price change rate.
We can get the annual rate of real price change (r) from a linearized model that allows us to keep the statistical robustness of a linear regression model (with statistics, confidence indicators and tests), but applying the compound rate approach used in mathematics of finance. To do that, the well-known basic formula for compounding Pn=P0 (1+r)^n, where:
   Pn = estimated price in year n
   P0= price in year 0
   r = annual rate of real price change (the real compound rate)
   n = number of years from year 0

is transformed into that of a straight line by making a change of variables (linearization).

The proposed method is easy to reproduce and seems more orthodox than apply projections made using a simple straight-line model. Even though the straight-line represents an average variation over the years, from a mathematics of finance approach we should discuss price variation in terms of the annual compound rate. In Figure 1, you can see the differences between these approaches.
If we have a clear trend in past real prices and the likelihood of a real price variation, we could make future price assumptions. If you agree with this statement and believe that price trend based on historical patterns is a significative information, then you should use r value gotten from the linearized model here proposed to project the price according to the previous compounding equation, where P0 is any real price calculated through the linearized compounding model (Table I).
In Catalonia, most of forest products prices have not kept up with inflation and reflect a declining trend. A few others have just barely kept up with inflation. This is means that, despite moderate growth in nominal terms, the real price of almost all Catalan forest products presents a negative trend. For example, Scots pine sawlogs -the most representative harvested species in Catalonia (the 27% of the total volume yearly logged)- have dropped by an average of almost 2% per year since 1980.
DOI: 10.17632/m2tytmjdzf.1
title: Pooled prevalence of Carpal Tunnel syndrome among dental health care providers

description: Continent: NA: North America; SA: South America; Eu: Europe; 
Method of diagnosis: SR: self-reported; NC: Nerve conduction; PE: Physical examination; V: Vibrometry; 
Type of dental personnel: DA: Dental Auxiliaries; D: Dentists/dental students, Mixed: Dental Auxiliaries or Dentists/dental students
Risk of bias: L: Low; M: Moderate
Risk of bias was assessed using nine-item questionnaire developed by Hoy et al.
DOI: 10.17632/m2tytmjdzf.2
title: Pooled prevalence of Carpal Tunnel syndrome among dental health care providers

description: Continent: NA: North America; SA: South America; Eu: Europe; 
Method of diagnosis: SR: self-reported; NC: Nerve conduction; PE: Physical examination; V: Vibrometry; 
Type of dental personnel: DA: Dental Auxiliaries; D: Dentists/dental students, Mixed: Dental Auxiliaries or Dentists/dental students
Risk of bias: L: Low; M: Moderate
Risk of bias was assessed using nine-item questionnaire developed by Hoy et al.
DOI: 10.17632/wg2kmmm3rz.1
title: Immunization during pregnancy: do healthcare workers recommend vaccination against influenza?

description: The dataset was used in a cross-sectional study aimed to assess if Italian healthcare workers recommend Tdap vaccination to pregnant women and which attitudes influence their practices. This  study took place between August 2021 and June 2022 in three regions of Southern Italy. An anonymous and self-administered questionnaire was distributed by a research team or sending it through a URL code via e-mail to gynecologists, midwives, general practiotioners. It  was composed of five sections: (1) the first section collected information about the sociodemographic and professional characteristics; (2) the second section investigated general knowledge about vaccinations against influenza and COVID-19 during pregnancy, as well as about those vaccine-preventable diseases (VPDs); (3) the third section measured attitudes towards immunization during pregnancy; (4) the fourth section explored pinvestigated HCWs’ immunization practices (i.e., advising and recommending pregnant people to get vaccinated against influenza) and strategies and interventions to potentially increase vaccination uptake during pregnancy; (5) the last section explored information sources, satisfaction with these sources, and the need for additional information about recommended vaccination during pregnancy.