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Quantum Resistance Corporation to Secure and Support Grantees … – PR Newswire

The Quantum Resistant Ledger (QRL) offers great potential for third-party projects to build DeFi, NFTs, DAOs, DEXs, gaming projects, and communications apps that are secure from post-quantum cryptography threats.

ZUG, Switzerland, April 5, 2023 /PRNewswire/ -- The Quantum Resistant Ledger (QRL) is investing significantly in applications and resources that can withstand the imminent threat of quantum computing advancements. Today, the QRL announced a grant to the Quantum Resistance Corporation (QRC) to provide a community security program for other QRL grantees, which are using the distributed network and post-quantum secure blockchain technology to securely build Layer2 applications and protocols. The QRL is the only blockchain that utilizes a signature scheme approved by the United States National Institute of Science and Technology (NIST) as being post-quantum secure.

The focus of the QRC grant project announced today includes a partnership with threat intelligence firm RedSense, to provide service for other QRL grantees. These services currently include netflow-based security for the distributed QRL environment, a community security program for QRL grant groups, and monitoring and security for all core QRL infrastructure. In time QRC will support the marketing and promotion of projects that result from QRL's work to grow the community of post-quantum secure developers and the offering of future-proof digital solutions. Early projects likely to receive funding include groups running computer systems for mining and building Layer 2 protocols with the QRL, which can opt into the security services and other support offered by QRC.

Growing the community of post-quantum secure developers and future-proof digital solutions.

"We are on the brink of the greatest shift in cryptography technology since the invention of the computer. Yet as this monumental shift is happening, the world is largely unaware," said Dr. Iain Wood. "That's why the QRL community is committed to supporting the top post-quantum secure distributed network and blockchain and empowering our community members to use the QRL technology to advance solutions for post-quantum secure environments."

Grants are available to those interested in building Layer 2 post-quantum secure applications. The goal of the QRL grant program is to generate projects in support of the QRL ecosystem in the areas of open source tools, education, open source infrastructure, post-quantum research, community, and public goods. The grant program is an opportunity to get involved with a cutting-edge open source project and build on the QRL to power the post-quantum secure smart contract platform. The goal is to grow the nascent post-quantum web3 ecosystem together as a community.

More about the QRL grant program including how to apply is here.

The QRCis the recipient of a $500,000 initial grant investment to encourage the use of the distributed QRL platform, community building, and security.

SOURCE The Quantum Resistance Corporation

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Skills shortages could pose threat to UKs quantum ambitions – IT PRO

A shortage of skilled quantum computing professionals in the UK has been identified as one of the key factors that could negatively influencethe nation's technological ambitions in the sector.

Speaking to IT Pro, Kate Marshall, quantum ambassador at IBM, said that while the UK currently holds a strong position in the global quantum computing space and has all the 'raw materials' for success, a stronger focus on developingskills will be required if the UK is to become a leading quantum economy.

Marshalls comments follow the governments Spring Statement, in which chancellor Jeremy Hunt outlined the next phase of the UKs quantum strategy.

Hunt told MPs that the government plans to commit 900 million in funding to implement recommendations outlined in the Future of Compute review to accelerate investment in quantum computing and deliver an exascale computer.

Marshall said the governments recent announcement marks a strong statement of intent, but warned a key hurdle the industry faces is whether the UK can produce a quantum-ready workforce.

In a 2021 study from Gartner, around 40% of large enterprises said they planned to start quantum computing initiatives by 2025.

However, another study from the consultancy revealed that only 6% of companies feel they already have the skills necessary to implement and deliver value from quantum computing.

Marshall said this highlights a disparity in workforce skills and warned that as businesses seek to embrace quantum computing over the next decade, many could be faced with significant skills-based challenges.

Theres a gap there, and I think this is about recognising that gap and making steps to close it as well, she said.

Theres definitely work to be done in terms of re-skilling those existing parts of the workforce that are very close to being able to work with this type of technology and get the most out of it, but theres a gap between where they need to be and where they are now.

Theres also a question around how we can make sure people who are currently in the education system - so in schools, colleges, and universities - are given the raw materials to succeed in this industry.

Moving forward, a heightened focus on skills and training relevant to the quantum computing space will be imperative, she said. Increased resources for people to upskill, reskill, and train for roles in the industry will also be crucial.

Theres the access question of whether people are actually going to learn to use these machines. They need access to whats available today.

"Then theres ecosystem management as well, this is something that industry, academia, and government are all facing at the same time. So theres definitely got to be some ecosystem coordination here.

Marshall said that although the blossoming UK quantum industry does face challenges, there are positive signs that the countrys academic infrastructure can produce a quantum-ready workforce.

The UK already boasts world-leading research and scientific capabilities through its academic institutions which will prove vital to supporting the future workforce and scaling the industry over the next decade.

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Our existing scientific and research excellence, the organisational structures of our top-class universities, and research spheres are definitely well placed to push this forward to make the UK a leader in this area, she said.

Theres definitely stuff that can be done to try and organise better, and that is what this ten-year vision and quantum strategy is hopefully going to be able to do, Marshall added.

The National Quantum Computing Centre, which was established in the previous five-year stage introduced in 2019, will also play a key role in helping to further develop the UKs quantum ecosystem and address some of these concerns around industry maturation and skills shortages, Marshall said.

Theyll be key in making the UK a more organised and stronger force in the global sphere of quantum computing and maximise our excellence in terms of academic research, but also organising and coordinating government engagement, industry venture investment, and then supply chain growth and international collaboration as well.

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Patanisho King Gidi Gidi Elated as He Graduates from University of Texas: "Certified Data Scientist" – Tuko.co.ke – Tuko.co.ke

The University of Texas, Austin has conferred celebrated radio presenter Gidi Gidi with a postgraduate degree.

The media personality popular for his morning show, Patanisho, took to Instagram to show off his certificate.

Gidi could not hide his joy while displaying his post-graduate programme in Data Science and Business Analytics.

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He captioned his post:

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Fellow celebrities trooped to Gidi's post to congratulate the radio king, and below are some of their comments below:

ghost_mulee wrote:

mcatricky wrote:

williamunga wrote:

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Microsoft and Jeff Bezos Tap Excel, Not Python Or R, To Teach Kids … – Slashdot

theodp writes: Are you ready to rock it with #datascience?" asks a tweet from Club for the Future, the tax-exempt foundation founded and funded by Jeff Bezos's Blue Origin, which is partnering with Microsoft's Hacking STEM to show how data science is used to determine a Go/No-Go launch of a Blue Origin New Shepard rocket. Interestingly, while Amazon founder Bezos and Microsoft CEO Satya Nadella are big backers of nonprofit Code.org and joined other tech CEOs for CS last fall to get the nation's Governors to "update the K-12 curriculum, for every student in every school to have the opportunity to learn computer science," Microsoft and Blue Origin have opted to teach kids aged 11-15 good old-fashioned Excel skills in their Introduction to the Data Science Process mini-course, not Python or R.

"Excel is a tool used around the world to work with data," Microsoft explains to teachers who have been living under a rock since 1985. "In these activities, students learn how to use Excel and complete all steps of a mission by engaging in the data science process. In this mission, students analyze key weather data in determining flight safety parameters for a New Shepard rocket and ultimately make a Go/No-Go decision for launch. Students learn how to use Excel while engaging in this dynamic Data Science Process activity [which is not unlike PLATO 'data science' activities of 50 years ago]." Blue Origin last September pledged to inspire youth to pursue space STEM careers as part of the Biden Administration's efforts to increase the space industry's capacity to meet the rising demand for the skilled technical workforce.

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Top 19 Skills You Need to Know in 2023 to Be a Data Scientist – KDnuggets

Times are changing. If you want to be a data scientist in 2023, there are several new skills you should add to your roster, as well as the slew of existing skills you should have already mastered.

Why such an extensive set of skills? Part of the problem is job scope creep. Nobody knows what a data scientist is, or what one should do, least of all your future employer. So anything that has data gets stuck in the data science category for you to deal with.

Youre expected to know how to clean, transform, statistically analyze, visualize, communicate, and predict data. Not only that but new technology (or technology that has recently reached the mainstream) could also be added to your job responsibilities.

In this article, Ill break down the top 19 skills you need to know in 2023 to be a data scientist.

Heres an overview of the ten most important.

These skills will help you land a job, crush an interview, stay ahead of the curve, and negotiate for that promotion. In each section, Ill briefly summarize what each skill is, why it matters, and offer a few places to learn these skills.

While its not 80% of a data scientists job, data cleaning and wrangling are still one of the most important skills a data scientist can master in 2023.

Data cleaning and wrangling are the processes of transforming raw data into a format that can be used for analysis. This involves handling missing values, removing duplicates, dealing with inconsistent data, and formatting the data in a way that makes it ready for analysis.

Cleaning the data usually refers to getting rid of bad/inaccurate values, filling in any blanks, finding duplicates, and otherwise making sure your data set is as spotless and reliably accurate as can be expected. Wrangling it (or munging it, massaging it, or any other weird verb like that) means getting it into an analyzable shape. You convert it or map it into another, easier-to-look-at-format.

Ask any data scientist what they do, and one of the first things they mention will be data cleaning and wrangling. Data never comes into your hands in a nice, clean, analyzable shape, so its super important to know how to get it tidy.

The ability to clean and wrangle data ensures that your analysis results are trustworthy, and helps to avoid incorrect conclusions being drawn.

There are plenty of great options to learn data cleaning and wrangling. Harvard offers a course on EdX. You can also practice on your own by cleaning and wrangling free, raw datasets like the Common Crawl, web crawl data composed of over 50 billion web pages (here), or Brazils weather data (here).

No, its not just a buzzword! Machine learning is a very important skill for any future data scientist to know.

Machine learning is the application of algorithms and statistical models to make predictions and decisions based on data.

Its a subfield of artificial intelligence that enables computers to improve their performance on a specific task by learning from data, without being explicitly programmed. It helps with automation. Youll find it in any industry.

You need to know about machine learning in 2023 because its a rapidly growing field that has become a crucial tool for solving complex problems and making predictions in various industries.

Machine learning algorithms can be used to classify images, recognize speech, do natural language processing, and create recommendation systems. Youll be hard-pressed to find an industry that doesnt do (or doesnt want to) do those ML-assisted tasks.

Being proficient in machine learning allows a data scientist to extract valuable insights from large and complex data sets, and to develop predictive models that can drive better business decisions.

Weve got a repository of over thirty machine-learning projects on ScrataScratch to show this skill off on your resume. TensorFlow also has a set of great free resources to learn machine learning.

This skill is pretty self-explanatory. When you analyze numbers, key stakeholders will want to understand your findings with pretty graphs and charts.

Data visualization is the creation of charts, graphs, and other graphics to help make data easier to understand. You take the numbers youve just cleaned, wrangled, or predicted and you put them into some kind of visual format, either to communicate trends with others or to make trends easier to spot.

In 2023, being able to visualize data is crucial for a data scientist. It's like having a secret superpower for uncovering hidden patterns and trends in the data that might not be obvious at first glance. And the best part? You get to share your findings with others in a way that's both engaging and memorable. As a data scientist, youll work with groups of all different experience levels, but a picture is much more easily understood than a row of numbers.

So, if you want to be a data scientist who can effectively communicate your insights and discoveries, it's important to master the art of data visualization.

Heres a list of free places to learn data viz.

SQL is a Structured Query Language. Data scientists use SQL to work with SQL databases as well as manage databases and perform data storage tasks.

SQL is a very popular language that lets you access and manipulate structured data. It goes hand in hand with database management, which is commonly done in SQL. Database management is basically how you can organize, store, and fetch data from a place. SQL databases are one of the top backend technologies to learn in 2023, so its not just for data science.

As a data scientist, you have to keep track of all the data, make sure it's organized, and retrieve it when someone needs it. Thats what SQL and database management let you do.

Coursera has a ton of great, well-priced database management/admin courses you can try. You can also get a sneak preview of some SQL interview questions here, which can be useful for testing your knowledge.

Big data is a buzzword, yes, but its also a real concept - Oracle defines it as data that contains greater variety, arriving in increasing volumes and with more velocity, or data with the three Vs.

Big data processing is the ability to process, store, and analyze large amounts of data using technologies like Hadoop and Spark.

In 2023, the ability to process big data is critical for data scientists. The volume of data being generated continues to grow at an exponential rate, and being able to handle and analyze this data effectively is essential for making informed decisions and gaining valuable insights. Data scientists who have a deep understanding of big data processing techniques will be able to work with large data sets with ease and make the most out of the information they contain.

Also, thanks to its buzz-wordiness, it never hurts to whack big data on your resume.

I love Simplilearns YouTube tutorial series on this concept.

Cloud computing is the use of cloud-based technologies and platforms like AWS, Azure, or Google Cloud to store and process data. Its kind of like having a virtual storage room that you can access from anywhere at any time. Instead of storing data and computing resources on local machines or servers, cloud computing allows organizations and data scientists to access these resources through the internet.

As I keep highlighting, the amount of data youre expected to work with as a data scientist is growing. More companies will be sticking it in the cloud rather than dealing with it on-prem. It's becoming increasingly important to have the ability to store and process this data in a scalable and efficient manner.

Cloud computing provides an effective solution for this, allowing data scientists to access vast amounts of computing resources and data storage without needing pricy hardware and infrastructure.

The good news is because companies own various clouds, many of them have a vested interest in teaching you about it for free, so you learn to use theirs. Google, Microsoft, and Amazon all have great cloud computing resources.

Wait, didnt we just cover databases? Whats a data warehouse? I hear you ask.

I get you. Sometimes it feels like the most critical data science skill is keeping all the acronyms and jargon straight.

First, lets differentiate data warehouses from databases.

Warehouses store current and historical data for multiple systems, while databases store current data needed to power a project. A database stores the current data required to power an application whereas a data warehouse stores current and historical data for one or more systems in a predefined and fixed schema to analyze the data.

In short, youd use a data warehouse for data for lots of different projects together, whereas a database mostly stores one single projects data.

ETL is a process that involves data warehousing, short for extract, transform, and load. An ETL tool will extract data from any data source systems you want, transform it in the staging area (usually cleaning, manipulating, or munging it), and then load it into a data warehouse.

I feel like Ive repeated this point in every skill, but data is growing. Companies are hungry for it, and theyll expect you to manage it. Knowing how to manage data in buildable pipelines is critical.

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The hardest part about working in AI and analytics ‘is the data itself’ – SiliconRepublic.com

Mastercards Ngoc Minh Tran discusses his role as lead data scientist and the growing importance of model explainability in AI and analytics.

Ngoc Minh Tran is a lead data scientist at Mastercard. Minh Tran began his career with a PhD in applied statistics/machine learning and has over 15 years of experience working in data science, with a focus on classical machine learning, deep learning and reinforcement learning.

With an impressive portfolio of achievements including being the lead inventor on 10 US patents in machine learning domains and the co-inventor of nine more we asked Minh Tran about what a typical day is like for a lead data scientist.

The most important thing for success in AI work is to practise as much as possible on multiple types of data

I always start a day with planning to work through the list of tasks under my responsibility, together with a target result or outcome. I also like to think about what tech book I am going to read if I manage to have some free time.

As a data scientist lead at Mastercard, I spend up to half of my day in meetings, both with my smaller tech team to support other data scientists to solve technical issues they are facing and with the wider Mastercard product teams to report or catch up on projects and plan for future projects.

The rest of the time is mainly for technical works such as architecting, designing, coding and problem solving.

Currently, machine learning and big data are the two AI skills that I am using the most in daily projects, in addition to SQL which is needed for analytics.

However, the most important elements that I am developing in my current role are management and communication skills. I try to pull key learnings that I see from experienced managers and apply them to manage my smaller tech team.

The hardest part is the data itself. Getting the correct data, and clean data, for the right project is important. This work cannot be solved alone, it needs support from the whole team.

An area of growing importance in the field of AI and data analytics is that of model explainability this concept relates to how we can explain or interpret, in human terms, how a model is reaching its prediction or decision. This is an important part of Mastercards commitment to ethical AI, and means that we need to consider how we can achieve an appropriate level of explainability when we embark on developing a new model.

If you have free time, try to read technical books. Its incredibly productive to use time to read while learning and improving your technical expertise.

Im a big believer in trying to automate your work as much as possible. Automating repetitive tasks will free you from any mistakes caused by human error while also saving you time and energy that can be used to do other tasks. In my work, I find that I can often use existing tools such as Jenkins and Docker to automate the CI pipeline for running unit tests, or Ill develop a simple tool in Python or some other scripting language to do it myself.

Tools like Jira, ALM and Confluence are very efficient for daily work collaboration. While Jira and ALM help to manage the progress of projects efficiently, Confluence is a place where you can store documents and share knowledge with your colleagues.

These tools are very useful for advancing the efficiency of our collaboration besides other common communication tools such as email, instant messaging and coffee tables.

In the past, data scientists were often familiar with data science skills but not engineering skills. However, full stack data scientists nowadays should train themselves for the engineering skills such as Docker containerisation, Kubernetes, AWS cloud and CICD (machine learning/dev-ops).

If you have free time, try to read technical books

It is also recommended that data scientists should practice standard coding styles (such as using Git professionally, following PEP8 rules, writing unit tests, etc.)

Joining an AI project and knowing that I am creating a product that is useful for our community makes me happy. I used to contribute to an open-source AI project which has thousands of users, so I feel like I am helping people to make the world a better place.

I also push myself to continuously innovate and I work hard to secure more and more patents for the company for the newest technology.

The most important thing for success in AI work is to practice as much as possible on multiple types of data. For example, joining Kaggle competitions is a good starting point for junior data scientists, where they have multiple types of free data to practise with. Furthermore, success in these competitions also gives people more confidence and advances their career. In addition, practical tutorials of deep learning frameworks such as Tensorflow and Pytorch are also helpful to practice in AI.

Dont forget to always be innovative writing patents will help refresh your mind and keep you motivated.

In addition, I recommend that people working in AI should read. I like to read good technical books and often read them twice. This habit helps extend your knowledge efficiently to help your job and your career in the long term. Pattern Recognition by Christopher Bishop is an enjoyable book that helps to master machine learning and I must have read this at least three times. Deep Learning by Ian Goodfellow, Aaron Courville and Yoshua Bengio is also a book that you cannot miss when working in AI.

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Srikripa Srinivasan: From chartered accountancy to data science – Indiatimes.com

Srikripa Srinivasan was a certified chartered accountant and cost accountant, and could have chosen to tie herself down a finance track after she started her career at accounting firm PwC three decades ago. But Srikripa is now vice president of Dell Global Analytics. She says she aligned her career choices in the direction in which technology evolved, allowing her to move away from routine spreadsheets and macros.She says data is the common factor binding the old bookkeeping ways with new-age, sophisticated databases. For her, it involved picking up new technologies and figuring out how to navigate her mothership finance into the new world of opportunities. And that journey evolved through stints in global majors like GlaxoSmithKline, GE and Microsoft.Today, Srikripas team of data scientists and data analysts in Bengaluru provides Dell with transformational support. The team uses analytics, AI and platforms to generate information and insights, to make the global IT hardware maker's business operations including supply-chain, marketing, sales and customer support cost-efficient and smart.Srikripa says while traditional mathematicians will continue to deliver a high degree of solutioning, the science behind presenting balanced information and insights from huge data sets can only be made possible by a data science team composed of people from diverse streams."Fashion retail, for example, is a domain where you can't just have a mathematician. You need people who understand fashion retail. So I over-index on having good data scientists from various streams," she says, underscoring the possibility of data science as a career opportunity for people who have majored in non-computer science streams.Srikripa says data scientists should be able to research data, and not get overwhelmed by data. She says they are expected to experiment and innovate. "So, fail fast, recover quickly, tweak," she says. Innovations, she says, must be impactful, and not be for the sake of innovating. "Like in Golf, if you are not scoring, you are just practising. We don't want practising innovators, we want impactful innovators," she says.Tools and AI-driven bots developed by Srikripas team help Dell's customer agents to respond to queries and resolve issues efficiently. The team has also developed intuitive tools to help Dell's sales operations. The tools predict what a potential customer may be looking to buy. The tools can also suggest products to sell under various price categories."None of this is rocket science, but when we put it in an organised manner, it makes the life of the seller much more efficient," she says.

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Diversity in German science: researchers push for missing ethnicity … – Nature.com

This article is part of a Nature series examining data on ethnic or racial diversity in science in different countries. See also: How UK science is failing Black researchers and How Indias caste system limits diversity in science.

In November 2021, Isolde Karles university in Germany appointed her to a newly created high-ranking role, which signalled the attention it was paying to diversity. As vice-rector for diversity, inclusion and talent development at the Ruhr University Bochum, Karles job involves helping students from minority groups including those discriminated against because of their skin colour to participate fully in science.

There is racism in society, and we universities are not free of it, says Karle, who is also a theologian and the universitys pastor.

Survey data support Karles concerns. This January, for instance, Germanys government issued its first annual report on racism. It cites, among other data, a large telephone survey that found more than one-fifth of people in the country had personally experienced racism (see go.nature.com/3zqsmvt; in German). And at universities, a Nature worldwide survey of graduate students last year found that, in Germany, 6% had experienced racial discrimination or harassment, but that this rose to 19% among those who identified as being in a racial or ethnic minority; findings similar to the surveys global results (see German graduate experiences).

Source: Nature graduate survey 2022 (Nature https://doi.org/j4cn; 2022)

Karle has talked to some researchers from minority ethnic groups at her university about their experiences. But she cant clearly track racial or ethnic diversity among the scholars at her institution or how it might be changing, because she has no data. That is the norm for employers all over Germany, where there is general unease at the idea of collecting information on ethnicity even though doing so is not legally prohibited as well as a feeling that it is not a priority issue, according to many Nature talked to.

Such concerns are often explained with reference to the countrys history and the horrific example of how the Nazis used census data to organize genocide. As a result, sensitivity over data protection and privacy is high: surveying people in Germany about their ethnicity has a strong negative connotation.

Karle expresses these reservations, too; asking employees about their race or heritage can itself be discrimination, she explains. We want to make the differences that lead to discrimination disappear, but by asking about them, we would focus on them, she says. This is part of the paradox.

The result in Germany is that, unlike in the United Kingdom, United States and several other countries, there are no census data on racial or ethnic diversity, and neither research funders nor universities collect this information. A spokesperson for the Bonn-based German Rectors Conference, an association of 269 universities in Germany, told Nature that there was no recording of personal characteristics that could identify minority ethnic groups for understandable and historical reasons, and because of data protection.

It is a similar situation in some other European countries: three research-funding agencies in France, Sweden and the Netherlands, for instance, told Nature that they dont collect diversity data on race or ethnicity, generally citing data-protection concerns.

Of course, this makes it difficult to quantitatively measure success on ethnic diversity issues, says a spokesperson for Germanys Max Planck Society (MPS) in Munich, a large public association of research institutes.

But this cultural unease is starting to shift. Some researchers in countries including Germany are pushing to gather data on ethnicity and race in academia, arguing that the advantages of quantitative data collection outweigh the concerns. In July 2022, Germanys largest public research funder, the DFG, issued guidelines that put a greater emphasis on ethnic diversity in data gathering.

For now, there are only snapshots of information. In 2020, an association of PhD students at the MPS, called Max Planck PhDnet, organized its own survey, asking more than 2,000 doctoral students about their citizenship and ethnicity (P.-G Majev et al. 2020 Survey Report https://doi.org/j39n; Max Planck PhDnet, 2021). It reported that 71% of the students who provided an ethnicity described themselves as of European descent, 10% as of East Asian or Southeast Asian descent, 7% as of South Asian descent and 0.7% (16 people) as of African descent. One-third of people describing themselves as non-European said they had felt discriminated against at work, compared with 18% of respondents identifying as European (see Representation among German doctoral students).

Source: P.-G Majev et al. https://doi.org/j39n (Max Planck PhDnet, 2021)

These findings are comparable to those in Natures 2022 survey: almost one-quarter of German respondents identified as being in an ethnic or racial minority and, of those, more than one-third said they had experienced discrimination or harassment (not always racial); compared with 17% of the other German respondents.

Larger surveys have tended to collect data using a coarse proxy called migration background. This typically refers to a person with at least one parent who was not born a German citizen which doesnt necessarily map well on to minority racial or ethnic status. In 2019, the German Centre for Higher Education Research and Science Studies (DZHW), a research institute in Hanover, launched a longitudinal study called the National Academics Panel Study that aims to examine the demographics and career paths of doctoral researchers in Germany. It has started to collect data on factors that include migration background and country of birth, but not ethnicity (see go.nature.com/3kt68jv; in German).

The DZHWs publicly available data define migration background as people who were born outside Germany. It found that this group accounted for 24% of some 14,000 doctoral researchers surveyed in 201920. (Data using the wider definition of migration background that is, including PhD students who have a parent born outside Germany are available to researchers on request, the DZHW says). In a separate study of around 18,000 German students (including undergraduates) last year, the DZHW found that 6% of all students said they had been discriminated against because of their migration background (see go.nature.com/3kgcdj7; in German).

How Indias caste system limits diversity in science in six charts

And last September, the German Education Union (GEW), which represents researchers and teachers, analysed data from multiple sources to suggest that 2% of German schoolchildren with a migration background might go on to gain a doctorate, whereas those with two German-born parents had double the chance, at 4% (see go.nature.com/3tzwcuj; in German). (The union added that the main differences occurred in transitions from primary school to high school, and from high school to bachelors degrees, rather than later in the academic pipeline.)

Such reports leave many questions about diversity in German science unanswered. But the picture might be changing. The Berlin non-governmental organization Citizens for Europe (CFE), for instance, aims to survey the full diversity of people in Germany including the interplay of race, culture and social class, as well as gender. In 2021, CFE and another Berlin-based non-governmental organization, Each One Teach One (EOTO), released the results of Afrozensus, a survey of more than 6,000 Black, African and Afro-Diasporic people in Germany, which aimed to raise awareness about the experiences of Black people in the country (see go.nature.com/3nptj9m; in German). It did not specifically analyse academic professions, but more than 4% of respondents had a PhD.

In all, only 15.3% of Afrozensus respondents said they had never been discriminated against in a professional context. The lack of data on race and discrimination is part of a vicious circle, says Daniel Gyamerah, who worked on the survey while at CFE. He is now head of a new non-profit think tank, called zedela (Centre for Data Driven Empowerment, Leadership and Advocacy), and is also EOTOs chairperson. Without data, it is hard to grasp the extent of discrimination problems, Gyamerah says, which then makes it harder to convince organizations and policymakers that they should track the issue. Reservations over data-protection laws are often used as an excuse to avoid engaging with the topic, he says.

Sociologist Lucienne Wagner, a CFE researcher who is pursuing a PhD at the University of Koblenz-Landau on the discourses of diversity at German universities, sees a further challenge for raising awareness about racial discrimination and collecting data on diversity at universities. With scant resources, people focused on gender equality can end up competing against rather than working with those exploring other dimensions of discrimination. But efforts in the field mean that attitudes in academia are changing, she adds.

The DFGs diversity guidelines, issued in July, could mark a significant shift. Transparency of equality requires the collection and publication of continuous, differentiated data at all academic career levels, in particular concerning the participation of women and men. The same applies to other dimensions of diversity as far as legally permissible, the guidelines say.

Asked for more details, the DFG told Nature that it doesnt have any specific rules on how to collect data on cultural or ethnic representation besides recommending anonymous surveys that pay careful attention to data protection. The funder says that it is up to universities to decide how to conduct such surveys and what data to collect, and that it doesnt have an overview of these activities. Universities and research institutions can and should already take measures to promote diversity in view of the evidence that is already available, a spokesperson said, referring to surveys by the DZHW and others. But the DFG also said that it is not aware of any universities already implementing such measures.

Last September, the German Rectors Conference launched a federally funded initiative called Diversity at German Universities. Details are limited, but the aim is to fund some universities to examine what hurdles and barriers need to be overcome to enable more diversity and inclusivity, with results due to be presented in 2024; monitoring diversity is also a goal.

How UK science is failing Black researchers in nine stark charts

For Joshua Kwesi Aikins, a political scientist and human-rights activist at the University of Kassel, who worked on the Afrozensus for CFE and is joining zedela, the time to collect information on race and on multiple forms of discrimination has already come. However, he adds, such data must be collected only with the full understanding and participation of the groups who are being asked for the information, so that the exercise is done in good faith.

History has led Germany to be rightfully cautious about racial classification. But German institutions need to act, he says. A strikingly male-dominated academe perpetuates racist, sexist and classist dynamics that add to structural hurdles on the way to what remains for too many a precarious academic career.

Aikins and Wagner are part of a three-year research network funded by the DFG and running until 2025, which aims to define new categories for data collection. This could include self-reported ethnicity, among other facets of identity. The group, coordinated by Anne-Kathrin Will, who studies migration at the Humboldt University of Berlin, and Linda Supik, a sociologist at Leibniz University in Hanover, is working with non-governmental and civil-society organizations.

Whether these efforts will change how German employers or universities collect race or ethnicity data remains to be seen. The DFG says that, in future, researchers wanting funding will need to address how they are promoting diversity, and that it plans to develop individual measures for this over a period of approximately five years. It also notes that it is developing a survey to understand needs and expectations related to its promotion of diversity, but says it cannot comment further.

At the Ruhr University Bochum, meanwhile, Karle says work is being done to improve the understanding of concerns over discrimination. Last July, for instance, Hans Alves, a psychologist at the university who specializes in social cognition, ran a survey of more than 4,000 students asking about their gender, migration status, religion and experiences of various kinds of discrimination. It did not ask about race or ethnicity. The results have not yet been published, but Alves told Nature the survey showed that 10% of students with a parent born outside Germany said they had been the target of discrimination because they were perceived as foreign or non-white. Karle says the university is now thinking about conducting a similar project across all of its academic staff.

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University of Birmingham wins 1.2m funding for AI and data … – University of Birmingham

The University of Birmingham has been awarded 1.2 million by the Office for Students (OfS) to fund up to 120 masters scholarships.

Published yesterday3 minute read

The scholarships are worth 10,000 each are are being offered for the 2023-24 academic year.

The award is part of 8.1 million funding from the Department for Science, Innovation and Technology (DSIT) and Office for Artificial Intelligence to encourage more women, Black students, disabled students, and students from lower socioeconomic backgrounds to study artificial intelligence (AI) and data science postgraduate programmes.

Students can study at a range of courses across England covering topics such as practical AI and data science skills, coding, programming, machine learning, health data science and AI ethics.

Professor Deborah Longworth, Pro-Vice Chancellor for Education, said: The University of Birmingham has designed a range of programmes to give students insight into the technologies transforming every sector, equipping them with the skills to put this knowledge into practice. This funding demonstrates the value we bring to our students and the industries they go on to work in and will give opportunities to students from underrepresented groups.

In 2019, the OfS launched a funding competition that aimed to increase diversity and address digital skills gaps in the workforce. The programme funded up to 1,000 scholarships at 28 universities. After a competitive selection process, the OfS will provide a further 8.1 million for up to 817 scholarships for the 2023-24 academic year. The University of Birmingham is one of 30 universities awarded a share of the funding to deliver scholarships to eligible underrepresented groups.

This funding demonstrates the value we bring to our students and the industries they go on to work n and will give opportunities to students from underrepresented groups.

John Blake, director for fair access and participation at the OfS, said: This funding provides opportunities for students underrepresented in these industries to achieve their career aspirations. It will enhance the relationships established between universities and employers that are vital for the success of this industry and provides the UKs data science and AI sector with a wider pool of highly skilled graduates.

Minister for AI at DSIT, Jonathan Camrose said: AI is increasingly being used to boost productivity and unlock growth in British industries. People from all walks of life should be able to access the exciting job opportunities this transformative technology is creating across the country. Were investing millions to champion people underrepresented in tech, so they get the skills to start a career in AI. I urge businesses to back the talent of tomorrow and offer their support for these important scholarships.

For media enquiries please contact Kathryn Hobbs, Press Office, University of Birmingham, Tel: +44 (0)7968 967837.

The University of Birmingham is ranked amongst the worlds top 100 institutions. Its work brings people from across the world to Birmingham, including researchers, teachers and more than 8,000 international students from over 150 countries.

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Slowly but surely, data is helping VCs look beyond networks for … – TechCrunch

Venture capital has traditionally been an industry that revolves around relationships. VCs invest in a startups idea but their conviction stems from the folks behind it. This largely makes sense because investing in a startup also usually entails entering a years-long relationship.

But backing companies based on the allure of the founder hasnt always worked out. Indeed, it often gets investors tied up in companies destined to collapse for one reason or another. And depending on warm intros or networks also limits the amount of startups an investor considers, which further alienates founders who dont have the same networks or hail from nontraditional backgrounds.

An increasing number of venture firms think the solution to cutting through the noise is by incorporating data science into their deal sourcing process. This wouldnt be a crazy idea per se, as investors from other asset classes such as institutional investors, hedge funds and public market traders already embrace data-driven investing, but thus far venture has largely sat out of the conversation.

A few venture firms, such as Correlation Ventures, SignalFire and Rocketship.vc, have long taken this approach, but this number looks likely to grow.

This week, Austin, Texas-based VC outlet Ensemble announced that it closed a $100 million debut fund to invest in early-stage startups using a data-driven approach that sorts and tracks companies based on the quality and depth of their entire team.

Ensembles co-founder and managing partner, Collin West an alum of Correlation Ventures told TechCrunch that the firm wants to back companies that have the strongest team, but that would be too difficult to track without using data science to pare the list down.

Using software, we can track all of the people at all of the startups, and that ends up being a whole lot more information than any human brain can handle, and especially any venture firm, West said. We effectively sort the industry by team quality in a very objective way knowing which companies to focus on, and spend a lot more time on fewer companies.

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