If the computed cell area is not desired then the CDO operator setgridarea can be used to set or overwrite the grid cell area. In the following situations it is necessary to give a description of a horizontal grid:. Predefined grids are available for global regular, gaussian, HEALPix or icosahedral-hexagonal GME grids. For the coordinates of a country CDO uses the DCW Digital Chart of the World dataset from GMT. The boundaries of each latitude are also generated.

The number of longitudes is 1. A grid description of this type is needed to calculate the zonal mean zonmean for data on an unstructured grid. XXX specifies the number of latitudes lines between the Pole and the Equator. The gaussian latitudes go from north to south. NI specifies the number of intervals on a main triangle side.

HEALPix is an acronym for Hierarchical Equal Area isoLatitude Pixelization of a sphere. The NSIDE parameter controls the resolution of the pixellization. It is the number of pixels on the side of each of the 12 top-level HEALPix pixels. ORDER sets the index ordering convention of the pixels, available are ring default or nest ordering. If the parameter b is set, the grid cell corners are also calculated.

You can use the grid description from an other datafile. The format of the datafile and the grid of the data field must be supported by CDO.

If there are more then one grid in the datafile the grid description of the first variable will be used. Add the extension :N to the name of the datafile to select grid number N. SCRIP Spherical Coordinate Remapping and Interpolation Package uses a common grid description for curvilinear and unstructured grids. For more information about the convention see [ SCRIP ]. This grid description is stored in NetCDF.

Therefor it is only available if CDO was compiled with NetCDF support! SCRIP grid description example of a curvilinear MPIOM [ MPIOM ] GROB3 grid only the NetCDF header :.

All supported grids can also be described with the CDO grid description. The following keywords can be used to describe a grid:. Which keywords are necessary depends on the gridtype. The following table gives an overview of the default values or the size with respect to the different grid types.

The keywords nvertex, xbounds and ybounds are optional if area weights are not needed. The grid cell corners xbounds and ybounds have to rotate counterclockwise. CDO grid description example of a T21 gaussian grid:. CDO grid description example of a global regular grid with 60x30 points:.

The description for a projection is somewhat more complicated. Use the first section to describe the coordinates of the projection with the above keywords. A list of attributes can be added to define the mapping.

The name of the attributes depend on the projection. The valid names of the projection and there attributes follow the NetCDF CF-Convention. These parameter will be passed directly to the PROJ library to generate the geographic coordinates if needed. Example CDO descriptions of a curvilinear and an unstructured grid can be found in Appendix D. The geographic coordinates of the ICON model are located on an unstructured grid. This grid is stored in a separate grid file independent of the model data.

The grid files are made available to the general public via a file server. Here is an example:. This attribute contains a link to the appropriate grid file on the ICON grid file server. The setgrid function is then no longer required.

If this environment variable is set, the grid file will be automatically downloaded from the grid file server to this directory if needed. If the grid file already exists in the current directory, the environment variable does not need to be set. If the grid files are available locally, like at DKRZ, they do not need to be fetched from the grid file server. Here is an example for the ICON grids at DKRZ:. Sometimes it is necessary to change the description of a z-axis.

This can be done with the operator setzaxis. This operator needs an ASCII formatted file with the description of the z-axis.

The following keywords can be used to describe a z-axis:. The keywords lbounds and ubounds are optional. vctsize and vct are only necessary to define hybrid model levels. Note that the vctsize is twice the number of levels plus two and the vertical coordinate table must be specified for the level interfaces. A time axis describes the time for every timestep.

Two time axis types are available: absolute time and relative time axis. CDO tries to maintain the actual type of the time axis for all operators.

An absolute time axis has the current time to each time step. It can be used without knowledge of the calendar. This is preferably used by climate models. A relative time is the time relative to a fixed reference time. The current time results from the reference time and the elapsed interval. The result depends on the calendar used. CDO supports the standard Gregorian, proleptic Gregorian, days, days and days calendars.

The relative time axis is preferably used by numerical weather prediction models. In NetCDF files the relative time axis is represented by the unit of the time: " time-units since reference-time ", e. g " days since ".

Some programs which work with NetCDF data can only process relative time axes. Therefore it may be necessary to convert from an absolute into a relative time axis. A parameter table is an ASCII formated file to convert code numbers to variable names.

Each variable has one line with its code number, name and a description with optional units in a blank separated list. It can only be used for GRIB, SERVICE, EXTRA and IEG formated files. Missing values are data points that are missing or invalid. Such data points are treated in a different way than valid data.

Most CDO operators can handle missing values in a smart way. But if the missing value is within the range of valid data, it can lead to incorrect results. This applies to all arithmetic operations, but especially to logical operations when the missing value is 0 or 1.

The default missing value for GRIB, SERVICE, EXTRA and IEG files is - 9. The CDO use of the missing value is shown in the following tables, where one table is printed for each operation.

The operations are applied to arbitrary numbers a , b , the special case 0 , and the missing value miss. For example the table named "addition" shows that the sum of an arbitrary number a and the missing value is the missing value, and the table named "multiplication" shows that 0 multiplied by missing value results in 0. The handling of missing values by the operations "minimum" and "maximum" may be surprising, but the definition given here is more consistent with that expected in practice.

Mathematical functions e. log , sqrt , etc. return the missing value if an argument is the missing value or an argument is out of range. All statistical functions ignore missing values, treading them as not belonging to the sample, with the side-effect of a reduced sample size.

An artificial distinction is made between the notions mean and average. The mean is regarded as a statistical function, whereas the average is found simply by adding the sample members and dividing the result by the sample size.

If there are no missing values in the sample, the average and mean are identical. There is no standard definition of percentile. All definitions yield to similar results when the number of values is very large. The following percentile methods are available in CDO :. The percentile method can be selected with the CDO option --percentile. The Nearest Rank method is the default percentile method in CDO.

The different percentile methods can lead to different results, especially for small number of data values. Consider the ordered list {15, 20, 35, 40, 50, 55}, which contains six data values. Here is the result for the 30th, 40th, 50th, 75th and th percentiles of this list using the different percentile methods:.

The amount of data for time series can be very large. All data values need to held in memory to calculate the percentile.

The percentile over timesteps uses a histogram algorithm, to limit the amount of required memory. The default number of histogram bins is That means the histogram algorithm is used, when the dataset has more than time steps. The histogram algorithm is implemented only for the Nearest Rank method. The CDO operators maskregion and selregion can be used to mask and select regions. For this purpose, the region needs to be defined by the user.

In CDO there are two possibilities to define regions. One possibility is to define the regions with an ASCII file. Each region is defined by a polygon. Each line of the polygon contains the longitude and latitude coordinates of a point. Here is a simple example of a polygon for a box with longitudes from W to 90E and latitudes from 20N to 20S:. With the second option, predefined regions can be used via country codes. Country codes can be combined with the plus sign.

This section gives a description of all operators. Related operators are grouped to modules. For easier description all single input files are named infile or infile1 , infile2 , etc. All output files are named outfile or outfile1 , outfile2 , etc. Further the following notion is introduced:. Timestep of infile. Element number of the field at timestep of infile. Timestep of outfile. Element number of the field at timestep of outfile. This section contains modules to print information about datasets.

All operators print there results to standard output. Here is a short overview of all operators in this section:. This module writes information about the structure and contents for each field of all input files to standard output. A field is a horizontal layer of a data variable.

All input files need to have the same structure with the same variables on different timesteps. The information displayed depends on the chosen operator.

Dataset information listed by parameter identifier Prints information and simple statistics for each field of all input datasets. For each field the operator prints one line with the following elements:.

Minimum, Mean and Maximum The mean value is computed without the use of area weights! Dataset information listed by parameter name The same as operator info but using the name instead of the identifier to label the parameter. Dataset information and simple map Prints information, simple statistics and a map for each field of all input datasets. This module writes information about the structure of infiles to standard output.

infiles is an arbitrary number of input files. Short information listed by parameter identifier Prints short information of a dataset. The information is divided into 4 sections. Section 1 prints one line per parameter with the following information:. Section 2 and 3 gives a short overview of all grid and vertical coordinates. And the last section contains short information of the time coordinate.

Short information listed by parameter name The same as operator sinfo but using the name instead of the identifier to label the parameter. Compares the contents of two datasets field by field. The input datasets need to have the same structure and its fields need to have the same header information and dimensions.

Try the option names if the number of variables differ. Exit status is 0 if inputs are the same and 1 if they differ. Compare two datasets listed by parameter id Provides statistics on differences between two datasets.

For each pair of fields the operator prints one line with the following information:. Compare two datasets listed by parameter name The same as operator diff. Using the name instead of the identifier to label the parameter. FLOAT Limit of the maximum absolute difference default: 0. FLOAT Limit of the maximum relative difference default: 1.

This is an example result of two datasets with one 2D parameter over 12 timesteps:. This module prints the number of variables, levels or times of the input dataset. Number of parameters Prints the number of parameters variables.

Number of levels Prints the number of levels for each variable. Number of years Prints the number of different years. Number of months Prints the number of different combinations of years and months.

Number of dates Prints the number of different dates. Number of gridpoints Prints the number of gridpoints for each variable. Number of horizontal grids Prints the number of horizontal grids. Show file format Prints the file format of the input dataset.

Show code numbers Prints the code number of all variables. Show variable names Prints the name of all variables.

Show standard names Prints the standard name of all variables. Show GRIB level types Prints the GRIB level type for all z-axes. Show date information Prints date information of all timesteps format YYYY-MM-DD. Show time information Prints time information of all timesteps format hh:mm:ss. Show timestamp Prints timestamp of all timesteps format YYYY-MM-DDThh:mm:ss.

showattribute [,attributes] infile. Use wildcards to print more than one attribute. This module provides operators to print meta information about a dataset. The printed meta-data depends on the chosen operator. Parameter table Prints all available meta information of the variables. Parameter code table Prints a code table with a description of all variables.

For each variable the operator prints one line listing the code, name, description and units. Grid description Prints the description of all grids. Z-axis description Prints the description of all z-axes. Vertical coordinate table Prints the vertical coordinate table.

Assume all variables of the dataset are on a Gausssian N16 grid. To print the grid description of this dataset use:. apply ,operators infiles. The apply utility runs the named operators on each input file.

The input files must be enclosed in square brackets. This utility can only be used on a series of input files. These are all operators with more than one input file infiles. Here is an incomplete list of these operators: copy, cat, merge, mergetime, select, ENSSTAT. The parameter operators is a blank-separated list of CDO operators. Use quotation marks if more than one operator is needed. Each operator may have only one input and output stream.

Suppose we have multiple input files with multiple variables on different time steps. The input files contain the variables U and V, among others. We are only interested in the absolute windspeed on all time steps. Here is the standard CDO solution for this task:. This first joins all the time steps together and then calculates the wind speed. If there are many variables in the input files, this procedure is ineffective.

In this case it is better to first calculate the wind speed:. However, this can quickly become very confusing with more than 3 input files. The apply operator solves this problem:. Another example is the calculation of the mean value over several input files with ensmean. The input files contain several variables, but we are only interested in the variable named XXX:. This module contains operators to copy, clone or concatenate datasets. Copy datasets Copies all input datasets to outfile.

Clone datasets Copies all input datasets to outfile. In contrast to the copy operator, clone tries not to change the input data. GRIB records are neither decoded nor decompressed. Concatenate datasets Concatenates all input datasets and appends the result to the end of outfile.

If outfile does not exist it will be created. If the output dataset already exists and you wish to extend it with more timesteps use:. tee ,outfile2 infile outfile1. This operator copies the input dataset to outfile1 and outfile2. The first output stream in outfile1 can be further processesd with other cdo operators. The second output outfile2 is written to disk. It can be used to store intermediate results to a file. STRING Destination filename for the copy of the input file.

pack infile outfile. Packing reduces the data volume by reducing the precision of the stored numbers. The default data type for all variables is automatically changed to bit integer. Use the CDO option -b to change the data type to a different integer precision, if needed.

Missing values are automatically transformed to the current data type. bitrounding [,params] infile outfile. This operator calculates for each field the number of necessary mantissa bits to get a certain information level in the data. With this number of significant bits numbits a rounding of the data is performed.

This allows the data to be compressed to a higher level. The default value of the information level is 0. That means Alternatively, the number of significant bits can be set for all variables with the numbits parameter. Furthermore, numbits can be assigned for each variable via the filename parameter.

In this case, numbits is still calculated for all variables if they are not present in the file. The analysis of the bit information is based on the Julia library BitInformation.

The procedure to derive the number of significant mantissa bits was adapted from the Python library xbitinfo. Quantize to the number of mantissa bits is done with IEEE rounding using code from NetCDF 4. Currently only bit float data is rounded. Data with missing values are not yet supported for the calculation of significant bits. FLOAT Information level 0 - 1 [default: 0. INTEGER Add bits to the number of significant bits [default: 0]. INTEGER Minimum value of the number of bits [default: 1].

INTEGER Maximum value of the number of bits [default: 23]. INTEGER Set to 1 to run the calculation only in the first time step. BOOL Print max. Apply bit rounding to all bit float fields, preserving replace infile1 infile2 outfile.

This operator replaces variables in infile1 by variables from infile2 and write the result to outfile. Both input datasets need to have the same number of timesteps. All variable names may only occur once! Assume the first input dataset infile1 has three variables with the names geosp, t and tslm1 and the second input dataset infile2 has only the variable tslm1.

To replace the variable tslm1 in infile1 by tslm1 from infile2 use:. duplicate [,ndup] infile outfile. This operator duplicates the contents of infile and writes the result to outfile. The optional parameter sets the number of duplicates, the default is 2.

mergegrid infile1 infile2 outfile. Merges grid points of all variables from infile2 to infile1 and write the result to outfile. Only the non missing values of infile2 will be used. The horizontal grid of infile2 should be smaller or equal to the grid of infile1 and the resolution must be the same.

Only rectilinear grids are supported. Both input files need to have the same variables and the same number of timesteps. This module reads datasets from several input files, merges them and writes the resulting dataset to outfile.

Merge datasets with different fields Merges time series of different fields from several input datasets. The number of fields per timestep written to outfile is the sum of the field numbers per timestep in all input datasets. The time series on all input datasets are required to have different fields and the same number of timesteps.

The fields in each different input file either have to be different variables or different levels of the same variable. A mixture of different variables on different levels in different input files is not allowed. Merge datasets sorted by date and time Merges all timesteps of all input files sorted by date and time. After this operation every input timestep is in outfile and all timesteps are sorted by date and time.

If set to 1, skips all consecutive timesteps with a double entry of the same timestamp. Operators of this module need to open all input files simultaneously. The maximum number of open files depends on the operating system! Assume three datasets with the same number of timesteps and different variables in each dataset.

To merge these datasets to a new dataset use:. Assume you split a 6 hourly dataset with splithour. This produces four datasets, one for each hour. The following command merges them together:. This module splits infile into pieces. xxx and the contents of the output files depends on the chosen operator.

params is a comma-separated list of processing parameters. Split code numbers Splits a dataset into pieces, one for each different code number. xxx will have three digits with the code number. Split parameter identifiers Splits a dataset into pieces, one for each different parameter identifier.

xxx will be a string with the parameter identifier. Split variable names Splits a dataset into pieces, one for each variable name. xxx will be a string with the variable name. Split levels Splits a dataset into pieces, one for each different level. xxx will have six digits with the level. Split grids Splits a dataset into pieces, one for each different grid. xxx will have two digits with the grid number.

Split z-axes Splits a dataset into pieces, one for each different z-axis. xxx will have two digits with the z-axis number. Split parameter table numbers Splits a dataset into pieces, one for each GRIB1 parameter table number.

xxx will have three digits with the GRIB1 parameter table number. STRING Swap the position of obase and xxx in the output filename.

Set the default file suffix. This suffix will be added to the output file names instead of the filename extension derived from the file format. Set this variable to NULL to disable the adding of a file suffix. Operators of this module need to open all output files simultaneously. Assume an input GRIB1 dataset with three variables, e.

code number , and To split this dataset into three pieces, one for each code number use:. splitmon [,format] infile obase. This module splits infile into timesteps pieces. Split hours Splits a file into pieces, one for each different hour. xxx will have two digits with the hour. Split days Splits a file into pieces, one for each different day. xxx will have two digits with the day.

Split seasons Splits a file into pieces, one for each different season. xxx will have three characters with the season. Split years Splits a file into pieces, one for each different year. xxx will have four digits with the year YYYY. Split in years and months Splits a file into pieces, one for each different year and month. xxx will have six digits with the year and month YYYYMM. Split months Splits a file into pieces, one for each different month.

xxx will have two digits with the month. STRING C-style format for strftime e. Assume the input GRIB1 dataset has timesteps from January to December.

To split each month with all variables into one separate file use:. splitsel ,nsets[,noffset[,nskip]] infile obase. INTEGER Number of input timesteps for each output file. INTEGER Number of input timesteps skipped before the first timestep range optional.

INTEGER Number of input timesteps skipped between timestep ranges optional. distgrid ,nx[,ny] infile obase. This operator distributes a dataset into smaller pieces.

Each output file contains a different region of the horizontal source grid. Data on an unstructured grid is split into nx pieces.

xxx will have five digits with the number of the target region. INTEGER Number of regions in x direction, or number of pieces for unstructured grids. INTEGER Number of regions in y direction [default: 1]. This operator needs to open all output files simultaneously.

On the left side is the data of the input file and on the right side is the data of the six output files. collgrid [,nx[,names]] infiles outfile. This operator collects the data of the input files to one output file. All input files need to have the same variables and the same number of timesteps on a different horizonal grid region. Data on an unstructured grid is concatenated in the order of the input files. The parameter nx needs to be specified only for curvilinear grids.

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Committee: UC Berkeley Custodians, Cooks, Groundskeepers and Nurse Assistants Supporting Aisha Wahab for Senate sponsored by American Federation of State, County and Municipal Employees Local - Committee: Californians for Better Priorities Supporting Dr.

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No committee supporting this ballot measure raised enough money to reach the reporting threshold for this list. For more information about the Top 10 lists and the relevant legal authorities, go to Methodology and Legal Resources.

Home Transparency Portal Top Contributor Lists November General Election. November General Election. Last Modified: December 15, The following key explains the symbols used to indicate changes after each new update. Status Key. Top Contributor Lists. Aggregate Contributions to Support or Oppose a Ballot Measure or Candidate Contributions to Committees Formed to Support or Oppose a Ballot Measure Contributions to Committees Primarily Formed to Make Independent Expenditures on Candidates The following are the total amount donated by top contributors to each state ballot measure that is pending Secretary of State verification or are still circulating for signatures.

Prop Circulating Title Top Aggregated Contributions 1 Constitutional Right to Reproductive Freedom. Legislative Constitutional Amendment. Initiative Constitutional Amendment and Statute. Initiative Statute. Proposition 1 SCA 10 Supporting Committee: Yes on Proposition 1, Protect Constitutional Abortion Rights, supported by health care organizations, Planned Parenthood Affiliates of California and Senator Toni Atkins Ballot Measure Committee - No.

Contributor State Status Total Contributions 1 Planned Parenthood Mar Monte, Inc. Contributor State Status Total Contributions 1 DaVita, Inc. Contributor State Status Total Contributions 1 Lyft, Inc.

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Additional Resources. Allows In-Person Roulette, Dice Games, Sports Wagering on Tribal Lands. Allows Online and Mobile Sports Wagering Outside Tribal Lands. Requires On-Site Licensed Medical Professional at Kidney Dialysis Clinics and Establishes Other State Requirements. Referendum On Law That Would Prohibit the Retail Sale of Certain Flavored Tobacco Products.

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California Nurses Association initiative Political Action Committee. Quinn Delaney. Planned Parenthood Advocacy Project Los Angeles County - Yes on Prop 1 nonprofit c 4 Top Donors to Contributor. Patty Quillin. Moving CA Forward a Committee Controlled by Anthony Portantino. Planned Parenthood Mar Monte, Inc. Planned Parenthood California Central Coast. Planned Parenthood of Orange and San Bernardino Counties Community Action Fund PAC Top Donors to Contributor.

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Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer.

In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Tarika Barrett centre says that the technology sector struggles to retain female employees. Credit: Girls Who Code. Children as young as six regard girls as being less interested than boys in computer science and engineering A.

Master et al. Natl Acad. USA , e; , and girls are less likely than boys to participate in advanced maths and computer-science classes and programmes. Women in science. Closing this gender gap will require more than just education and training.

It will also require mentorship and a professional network. The group is one of several that aim to help women from under-represented communities to tap into the social capital necessary for workplace success in computer science. These groups help students to prepare for interviews, share job opportunities and study collaboratively on advanced courses.

Perhaps most importantly, they provide a space for women, trans men and non-binary people, who experience the brunt of gender-based discrimination, to vent and share their workplace struggles.

Several of these communities have developed robust online communities on collaboration platforms such as Slack, through which they aim to engage and support their members. Part of what we do at Girls Who Code is teach computer science. Marginalized students who lack either the social capital or the connections to get this help are left on the sidelines. So, we created an interview-preparation programme to help to level that playing field.

That can be alienating for people of colour, especially when they get their first job, but even more so for women of colour, because of the compounding effects of racism and sexism. We did a survey in and found that half of the women applying for tech internships either had a negative experience of the industry or knew someone who did.

Those experiences ranged from a lack of representation, to sexist and racist comments, to actual harassment. There is a severe retention issue. Half of women leave tech by the age of 35 because they find their workplace inhospitable. Gabriela de Queiroz wants to improve diversity in users of the programming language R. Credit: Gabriela de Queiroz.

Founder of R-Ladies and a principal cloud advocate at Microsoft in San Francisco, California. Soon after, I started going to lots of events to learn about new technology, but networking was hard. After a few months, I wanted to contribute more. People were sharing their knowledge in their spare time, and I wondered, is there anything I can share with others? I wanted to create something more inclusive and more diverse around the statistical programming language R.

At one of our first events, we created a study group in which we would work through online courses together. Less than a year later, someone from Taiwan e-mailed saying they wanted to start an R-Ladies chapter in Taipei. Now there are chapters in cities in 61 countries, with more than , members. We have Slack channels to communicate, and guides and resources to help chapters to stay active. One key aspect when we set up the community was making sure that everybody felt welcome, no matter their gender, background, ethnicity or anything else.

I greet everyone at events, and we explain our code of conduct each time. Tina Lee wants to give mothers easier access to coding opportunities. Credit: Tina Lee. Founder of MotherCoders and is director of special projects at Bitwise Industries in San Francisco, California. I remember going to a coding workshop over a weekend because the organizers said there was childcare. I held her in one arm and typed with the other for the rest of the workshop.

I started MotherCoders in November I spread the word through e-mail, Facebook and Twitter, and a few months later we ran a pilot programme in a co-working space in San Francisco that was empty on the weekends. My second child was about six months old then. We had childcare on one side and the mothers learning to code on the other. The arrangement gave them a safe space to learn, and there was no stigma associated with having your child on site.

My daughter was there while I taught the classes. But fundraising was really hard. But trying to raise money from companies and high-net-worth individuals for MotherCoders was a completely different ball game.

I was starting from scratch. In our second year, MotherCoders was a finalist in the Google Impact challenge and received a grant. But we were treading water for a long time. After six years — many of which I worked without a salary — I got tired of pushing this rock up a hill.

That is how MotherCoders became the non-profit arm of Bitwise Industries. Bitwise is a for-profit company, but MotherCoders is very mission-aligned with it, in that the company is training underserved communities and running apprenticeships and so on. What really sealed the deal for me is that the firm plans to provide on-site childcare at every centre it operates, which was always my dream.

Dedicated space is key, says Caitlin Hudon. Credit: Caitlin Hudon. Founder of ALL the Ladies in Tech and the R-Ladies chapter in Austin, Texas, and a data scientist at Figma in Austin. I reached out to the organizers of all these groups and set up a quarterly mega gathering called ALL the Ladies in Tech, at which each group talks about what they have coming up during that period. We make announcements about jobs, training opportunities — anything relevant to this larger community.

The goal is to get everyone in the same room to network and learn about all the opportunities. I also co-founded the R-Ladies chapter in Austin, Texas, which took a lot of forethought and work.

Trying to understand what will appeal and then changing the content, scheduling or location to fit those parameters is a bit of a learning curve. For instance, we think about what resources we should share after an event. But for women and people from minority groups, you have to work harder to meet people like yourself. Having a dedicated space, in which people can ask questions, get feedback and have the freedom to be more vulnerable than they might be otherwise, is a really healthy, important thing.

Analyse attendance. Attrition in technology begins in primary school and continues through university. Examining classroom participation can reveal problem areas. If you offer CS, an intermediate class and an advanced one, what are your retention levels?

Take more questions from women. Asking questions at conferences helps people to gain confidence. But even when women make up the majority at a meeting, they tend to ask fewer questions than do male attendees. Prioritizing women and people from minority groups can help.

Be transparent. One way to close that gap is to remove the veil of secrecy around salary. That has helped her to negotiate her own benefits more effectively.

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Since she has been running Sonic Bloom, the biggest independent online resource on all things Ableton. CIFAP is a leading provider of high-quality professional training for workers in the creative industries. Sergio started playing with all kinds of instruments and circuits at a young age and today, as a sound engineer, computer scientist, musician and producer, he remains deeply focused on the creative processes of sound. Set variable unit Sets the unit of the first variable. Some of the CDO operators are shared memory parallelized with OpenMP. Further the following notion is introduced:.

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