Shown are mean±SD. Values were extrapolated from a smaller temperature range, using linear or exponential fits (details in Materials and Methods and Table S1). 1: As defined by the onset time of the impulse response, K1. 2: Characteristic time-constant defined as: τ = (f3dB)−1. 3: Information transfer rate (Shannon's formula). 4: Information transfer rate (triple extrapolation method).

Gamification is a strategy to stimulate social and human factors (SHF) that influence software development productivity. However, software development teams must improve their productivity to face the challenges of software development organizations. Traditionally, productivity analysis only includes technical factors. Literature shows the importance of SHFs in productivity. Furthermore, gamification elements can contribute to enhancing such factors to improve performance. Thus, to design strategies to enhance a specific SHF, it is essential to identify how gamification elements are related to these factors. The objective of this research is to determine the relationship between gamification elements and SHF that influence the productivity of software development teams. This research included the design of a scoring template to collect data from the experts. The importance was calculated using the Simple Additive Weighting (SAW) method as a tool framed in decision theory. Three criteria were considered: cumulative score, matches in inclusion, and values. The relationships of importance serve as a reference value in designing gamification strategies that promote improved productivity. It extends the path toward analyzing the effect of gamification on the productivity of software development. This relationship facilitates designing and implementing gamification strategies to improve productivity.

EMG data were normalized using Max-Min strategy. For comparison across all subjects, ʃIEMG values were normalized through following formula. the result of this equation ranged all the ʃIEMG values in to -1 to +1 ʃIEMGN = ʃIEMGi / ʃIEMGMAX

With a Monte Carlo code ACAT, we have calculated sputtering yield of fifteen fusion-relevant mono-atomic materials (Be, B, C, Al, Si, Ti, Cr, Fe, Co, Ni, Cu, Zr, Mo, W, Re) with obliquely incident light-ions H+, D+, T+,, He+) at incident energies of 50 eV to 10 keV. An improved formula for dependence of normalized sputtering yield on incident-angle has been fitted to the ACAT data normalized by the normal-incidence data to derive the best-fit values of the three physical variables included in the formula vs. incident energy. We then have found suitable functions of incident energy that fit these values most closely. The average relative difference between the normalized ACAT data and the formula with these functions has been shown to be less than 10 % in most cases and less than 20 % for the rest at the incident energies taken up for all of the combinations of the projectiles and the target materials considered. We have also compared the calculated data and the formula with available normalized experimental ones for given incident energies. The best-fit values of the parameters included in the functions have been tabulated in tables for all of the combinations for use.

The Saha equation describes the relative number density of consecutive ionization levels of a given atomic species under conditions of thermodynamic equilibrium in an ionized gas. Because the number density in the denominator may be very small, special steps must be taken to ensure numerical stability. In this paper we recast the equation into a form in which each ionization fraction is normalized by the total number density of the atomic species, analogous to the Boltzmann equation describing the distribution of excitation states for a given ion.

This dataset includes intermediate data from RiboBase that generates translation efficiency (TE). The code to generate the files can be found at https://github.com/CenikLab/TE_model.

We uploaded demo HeLa .ribo files, but due to the large storage requirements of the full dataset, I recommend contacting Dr. Can Cenik directly to request access to the complete version of RiboBase if you need the original data.

The detailed explanation for each file:

human_flatten_ribo_clr.rda: ribosome profiling clr normalized data with GEO GSM ids in columns and genes in rows in human.

human_flatten_rna_clr.rda: matched RNA-seq clr normalized data with GEO GSM ids in columns and genes in rows in human.

human_flatten_te_clr.rda: TE clr data with GEO GSM ids in columns and genes in rows in human.

human_TE_cellline_all_plain.csv: TE clr data with genes in rows and cell lines in rows in human.

human_RNA_rho_new.rda: matched RNA-seq proportional similarity data as genes by genes matrix in human.

human_TE_rho.rda: TE proportional similarity data as genes by genes matrix in human.

mouse_flatten_ribo_clr.rda: ribosome profiling clr normalized data with GEO GSM ids in columns and genes in rows in mouse.

mouse_flatten_rna_clr.rda: matched RNA-seq clr normalized data with GEO GSM ids in columns and genes in rows in mouse.

mouse_flatten_te_clr.rda: TE clr data with GEO GSM ids in columns and genes in rows in mouse.

mouse_TE_cellline_all_plain.csv: TE clr data with genes in rows and cell lines in rows in mouse.

mouse_RNA_rho_new.rda: matched RNA-seq proportional similarity data as genes by genes matrix in mouse.

mouse_TE_rho.rda: TE proportional similarity data as genes by genes matrix in mouse.

All the data was passed quality control. There are 1054 mouse samples and 835 mouse samples:
* coverage > 0.1 X
* CDS percentage > 70%
* R2 between RNA and RIBO >= 0.188 (remove outliers)

All ribosome profiling data here is non-dedup winsorizing data paired with RNA-seq dedup data without winsorizing (even though it names as flatten, it just the same format of the naming)

####code
If you need to read rda data please use load("rdaname.rda") with R

If you need to calculate proportional similarity from clr data:
library(propr)
human_TE_homo_rho <- propr:::lr2rho(as.matrix(clr_data))
rownames(human_TE_homo_rho) <- colnames(human_TE_homo_rho) <- rownames(clr_data)

RAW data, lens crossection, normalization calculation

This visualization product displays marine macro-litter (> 2.5cm) material categories percentage per beach per year from Marine Strategy Framework Directive (MSFD) monitoring surveys. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale. Preliminary processing were necessary to harmonize all the data: - Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring; - Selection of MSFD surveys only (exclusion of other monitoring, cleaning and research operations); - Exclusion of beaches without coordinates; - Some litter types like organic litter, small fragments (paraffin and wax; items > 2.5cm) and pollutants have been removed. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines and EU Threshold Value for Macro Litter on Coastlines from JRC (these two documents are attached to this metadata); - Exclusion of the "feaces" category: it concerns more exactly the items of dog excrements in bags of the OSPAR (item code: 121) and ITA (item code: IT59) reference lists; - Normalization of survey lengths to 100m & 1 survey / year: in some case, the survey length was not exactly 100m, so in order to be able to compare the abundance of litter from different beaches a normalization is applied using this formula: Number of items (normalized by 100 m) = Number of litter per items x (100 / survey length) Then, this normalized number of items is summed to obtain the total normalized number of litter for each survey. Sometimes the survey length was null or equal to 0. Assuming that the MSFD protocol has been applied, the length has been set at 100m in these cases. To calculate percentages for each material category, formula applied is: Material (%) = (?number of items (normalized at 100 m) of each material category)*100 / (?number of items (normalized at 100 m) of all categories) The material categories differ between reference lists (OSPAR, ITA, TSG_ML, UNEP, UNEP_MARLIN). In order to apply a common procedure for all the surveys, the material categories have been harmonized. More information is available in the attached documents. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.

This visualization product displays marine macro-litter (> 2.5cm) material categories percentages per beach per year from non-MSFD monitoring surveys, research & cleaning operations.

EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale.

Preliminary processings were necessary to harmonize all the data: - Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring; - Selection of surveys from non-MSFD monitoring, cleaning and research operations; - Exclusion of beaches without coordinates; - Exclusion of surveys without associated length; - Some litter types like organic litter, small fragments (paraffin and wax; items > 2.5cm) and pollutants have been removed. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines, the European Threshold Value for Macro Litter on Coastlines and the Joint list of litter categories for marine macro-litter monitoring from JRC (these three documents are attached to this metadata); - Exclusion of the "feaces" category: it concerns more exactly the items of dog excrements in bags of the OSPAR (item code: 121) and ITA (item code: IT59) reference lists; - Normalization of survey lengths to 100m & 1 survey / year: in some case, the survey length was not 100m, so in order to be able to compare the abundance of litter from different beaches a normalization is applied using this formula: Number of items (normalized by 100 m) = Number of litter per items x (100 / survey length) Then, this normalized number of items is summed to obtain the total normalized number of litter for each survey.

To calculate the percentage for each material category, formula applied is: Material (%) = (∑number of items (normalized at 100 m) of each material category)*100 / (∑number of items (normalized at 100 m) of all categories)

The material categories differ between reference lists (OSPAR, TSG-ML, UNEP, UNEP-MARLIN, JLIST). In order to apply a common procedure for all the surveys, the material categories have been harmonized.

More information is available in the attached documents.

Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the Marine Litter Database for this area.

This visualization product displays the cigarette related items abundance of marine macro-litter (> 2.5cm) per beach per year from non-MSFD monitoring surveys, research & cleaning operations without UNEP-MARLIN data.

EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB).

The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale.

Preliminary processings were necessary to harmonize all the data:

• Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring;

• Selection of surveys from non-MSFD monitoring, cleaning and research operations;

• Exclusion of beaches without coordinates;

• Selection of cigarette related items only. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines, the European Threshold Value for Macro Litter on Coastlines and the Joint list of litter categories for marine macro-litter monitoring from JRC (these three documents are attached to this metadata);

• Exclusion of surveys without associated length;

• Exclusion of surveys referring to the UNEP-MARLIN list: the UNEP-MARLIN protocol differs from the other types of monitoring in that cigarette butts are surveyed in a 10m square. To avoid comparing abundances from very different protocols, the choice has been made to distinguish in two maps the cigarette related items results associated with the UNEP-MARLIN list from the others;

• Normalization of survey lengths to 100m & 1 survey / year: in some case, the survey length was not 100m, so in order to be able to compare the abundance of litter from different beaches a normalization is applied using this formula:

Number of cigarette related items of the survey (normalized by 100 m) = Number of cigarette related items of the survey x (100 / survey length)

Then, this normalized number of cigarette related items is summed to obtain the total normalized number of cigarette related items for each survey. Finally, the median abundance of cigarette related items for each beach and year is calculated from these normalized abundances of cigarette related items per survey.

Percentiles 50, 75, 95 & 99 have been calculated taking into account cigarette related items from other sources data (excluding UNEP-MARLIN protocol) for all years.

More information is available in the attached documents.

Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the Marine Litter Database for this area.

This visualization product displays the plastic bags abundance of marine macro-litter (> 2.5cm) per beach per year from non-MSFD monitoring surveys, research & cleaning operations. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale. Preliminary processing were necessary to harmonize all the data: - Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring; - Selection of surveys from non-MSFD monitoring, cleaning and research operations; - Exclusion of beaches without coordinates; - Selection of plastic bags related items only. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines and EU Threshold Value for Macro Litter on Coastlines from JRC (these two documents are attached to this metadata); - Exclusion of surveys without associated length; - Normalization of survey lengths to 100m & 1 survey / year: in some case, the survey length was not 100m, so in order to be able to compare the abundance of litter from different beaches a normalization is applied using this formula: Number of plastic bags related items of the survey (normalized by 100 m) = Number of plastic bags related items of the survey x (100 / survey length) Then, this normalized number of plastic bags related items is summed to obtain the total normalized number of plastic bags related items for each survey. Finally, the median abundance of plastic bags related items for each beach and year is calculated from these normalized abundances of plastic bags related items per survey. Percentiles 50, 75, 95 & 99 have been calculated taking into account plastic bags related items from other sources data for all years. More information is available in the attached documents. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.

This visualization product displays the total abundance of marine macro-litter (> 2.5cm) per beach per year from non-MSFD monitoring surveys, research & cleaning operations.


EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB).
The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale.


Preliminary processing were necessary to harmonize all the data:
- Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring;
- Selection of surveys from non-MSFD monitoring, cleaning and research operations;
- Exclusion of beaches without coordinates;
- Some categories & some litter types like organic litter, small fragments (paraffin and wax; items > 2.5cm) and pollutants have been removed. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines and EU Threshold Value for Macro Litter on Coastlines from JRC (these two documents are attached to this metadata).
- Exclusion of surveys without associated length;
- Normalization of survey lengths to 100m & 1 survey / year: in some case, the survey length was not 100m, so in order to be able to compare the abundance of litter from different beaches a normalization is applied using this formula:
Number of items (normalized by 100 m) = Number of litter per items x (100 / survey length)
Then, this normalized number of items is summed to obtain the total normalized number of litter for each survey. Finally, the median abundance for each beach and year is calculated from these normalized abundances per survey.

Percentiles 50, 75, 95 & 99 have been calculated taking into account other sources data for all years.


More information is available in the attached documents.


Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.

This visualization product displays marine litter material categories percentage per year per beach from research & cleaning operations. EMODnet Chemistry included the gathering of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale. Preliminary processing were necessary to harmonize all the data : - Exclusion of OSPAR 1000 protocol, - Separation of monitoring surveys from research & cleaning operations - Exclusion of beaches with no coordinates - Normalization of survey lengths and survey numbers per year - Some categories & some litter types have been removed To calculate percentages, formula applied is : Material (%) = (total number of items (normalized at 100 m) of each material category)/(total number of items (normalized at 100 m) of all categories)*100 The material categories differ between reference lists. In order to apply a common procedure for all the surveys, the material categories have been harmonized. Eleven material categories have taken into account for this product and information on data processing and calculation are detailed in the document attached p14.

This visualization product displays the spatial distribution of seafloor litter density per trawl.

EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of seafloor litter collected by international fish-trawl surveys have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols (OSPAR and MEDITS protocols) and reference lists used on a European scale. Moreover, within the same protocol, different gear types are deployed during fishing bottom trawl surveys.

In cases where the wingspread and/or number of items were unknown, data could not be used because these fields are needed to calculate the density. Data collected before 2011 are affected by this filter.

When the distance reported in the data was null, it was calculated from: - the ground speed and the haul duration using this formula: Distance (km) = Haul duration (h) * Ground speed (km/h); - the trawl coordinates if the ground speed and the haul duration were not filled in.

The swept area is calculated from the wingspread (which depends on the fishing gear type) and the distance trawled: Swept area (km²) = Distance (km) * Wingspread (km)

Densities have been calculated on each trawl and year using the following computation: Density (number of items per km²) = ∑Number of items / Swept area (km²)

Then a grid with 30km x 30km cells is used to calculate the weighted mean of densities in each cell from the formula : Weighted mean (number of items per km²) = ∑ (Distance (km) * Density (number of items per km²)) / ∑ Distance (km)

Percentiles 50, 75, 95 & 99 have been calculated taking into account data for all years.

More information on data processing and calculation are detailed in the document attached.

Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.

This work is based on the work presented in the following scientific article: O. Gerigny, M. Brun, M.C. Fabri, C. Tomasino, M. Le Moigne, A. Jadaud, F. Galgani, Seafloor litter from the continental shelf and canyons in French Mediterranean Water: Distribution, typologies and trends, Marine Pollution Bulletin, Volume 146, 2019, Pages 653-666, ISSN 0025-326X, https://doi.org/10.1016/j.marpolbul.2019.07.030.