21 Open Access Books on AI, Software, Robots & Business you can klick on – Part 2

Dear guest!

This is part 2 of my list with another 10 open access books on Artificial Intelligence, software, robots, business, and related fields.

Klick here for part 1 of my list with the first 11 entries introduced by a text about the importance of media like libraries, the World Wide Web, and the (open) access to data, information, and knowledge.

So enjoy, select, klick on, and read one ore more of the following books!

12) Software Engineering and Data Science (2023)

Editor: Tosi, Davide

Link: https://directory.doabooks.org/handle/20.500.12854/96748

13) Advancing Natural Language Processing in Educational Assessment (2023)

Authors: Yaneva, Victoria; von Davier, Matthias

Link: https://directory.doabooks.org/handle/20.500.12854/100384

14) Robots and AI (2023)

Authors: Ing, Lili Yan; Grossman, Gene M.

Link: https://directory.doabooks.org/handle/20.500.12854/82165

15) Artificial Intelligence and International Conflict in Cyberspace (2023)

Authors: Cristiano, Fabio; Broeders, Dennis; Delerue, François; Douzet, Frédérick; Géry, Aude

Link: https://directory.doabooks.org/handle/20.500.12854/99946

16) Emotion Recognition (2023)

Author: Abed Hosseini, Seyyed

Link: https://directory.doabooks.org/handle/20.500.12854/113472

17) Trends and Challenges in Robotic Applications(2023)

Authors: Gracia, Luis; Perez-Vidal, Carlos

Link: https://directory.doabooks.org/handle/20.500.12854/113962

18) The 2nd International Conference on Computational Engineering and Intelligent Systems (2023)

Authors: Recioui, Abdelmadjid; Bentarzi, Hamid; Dekhandji, Fatma

Link: https://directory.doabooks.org/handle/20.500.12854/113891

19) Intelligent Video Surveillance (2023)

Author: Luigi Mazzeo, Pier

Link: https://directory.doabooks.org/handle/20.500.12854/113204

20) Malware (2023)

Author: Babulak, Eduard

Link: https://directory.doabooks.org/handle/20.500.12854/113227

21) Internet of Things (2023)

Authors: Domínguez-Morales, Manuel; Varela-Vaca, Ángel; Miró-Amarante, Lourdes

Link: https://directory.doabooks.org/handle/20.500.12854/113202

I wish you a nice day!

Stefan Klemens

PS: Do you want to exchange ideas about human resource management, People Analytics, Digital Assessment, or Artificial Intelligence in HRM? Then network, send a message and/or schedule an online meeting. Or the classic way: a phone call.

And: Do you like my work and the content I regularly share? Then I’m happy about a Like or comment on LinkedIn. Thank you! 🙂 🙋‍♂️🌳


21 Open Access Books about AI, Data, Robots & Business you can klick on – Part 1

Dear guest!

One thing we should remember: Thanks to the widespread and more public available libraries since the beginning of the print book at the start of the Renaissance until the spread of information through the World Wide Web since the 1991:

The access to knowledge has grown dramatically to almost anyone in the world and this stands in sharp contrast to the keeping of knowledge in secret and for a few selected people in the past. But thanks to technology like letterpress printing (credits of course in the west to Johannes G.) and the work of Tim Berners-Lee for the WWW: data, information, and knowledge is now in our Information Age at almost any place in the world and at any time available.

Which can be of course a challenge and leads sometimes even to internet addiction when digital detoxing is not done in time. But I am not an expert in Clinical Psychology, so turn to some other expertise in case of interest in this matter.

Book recommendations: Surely you find helpful insights on “how to stay smart in a smart world” in the same named book by renowned Gerd Gigerenzer (see also his new 2023 published book The Intelligence of Intuition for a deeper understanding of human decision making; Note: free access to Chapter 1).

In the last twenty years another trend emerged and the publication of research papers and textbooks by open access lowers sometimes expensive orders of magazines and textbooks by publishers. One great source for this is the Directory of Open Access Books (doab), which I used for the research on the theme of this newsblog article.

So enjoy, select, klick on, and read one ore more of the following books on Artificial Intelligence, software, robots, business, and related fields.

1) Business Data Ethics (2024)

Authors: Hirsch, Dennis; Bartley, Timothy; Chandrasekaran, Aravind; Norris, Davon; Parthasarathy, Srinivasan; Turner, Piers Norris

Link: https://directory.doabooks.org/handle/20.500.12854/131983

2) A Circular Built Environment in the Digital Age (2024)

Editors: De Wolf, Catherine; Çetin, Sultan; Bocken, Nancy M. P.

Link: https://directory.doabooks.org/handle/20.500.12854/133078

3) Introduction to Digital Humanism (2024)

Editors: Werthner, Hannes; Ghezzi, Carlo; Kramer, Jeff; Nida-Rümelin, Julian; Nuseibeh, Bashar; Prem, Erich; Stanger, Alliso

Link: https://directory.doabooks.org/handle/20.500.12854/133034

4) Internet of Production (2024)

Editors: Brecher, Christian;Schuh, Günther;van der Aalst, Wil; Jarke, Matthias; Piller, Frank T.; Padberg, Melanie

Link: https://directory.doabooks.org/handle/20.500.12854/133035

5) Multidisciplinary Perspectives on Artificial Intelligence and the Law (2024)

Editors: Sousa Antunes, Henrique; Freitas, Pedro Miguel; Oliveira, Arlindo L.; Martins Pereira, Clara; Vaz de Sequeira, Elsa; Barreto Xavier, Luís

Link: https://directory.doabooks.org/handle/20.500.12854/133041

6) Shaping the Future of IoT with Edge Intelligence (2024)

Editiors: C. Sofia, Rute & Soldatos, John

Link: https://directory.doabooks.org/handle/20.500.12854/122405

7) Global Digital Data Governance: Polycentric Perspectives (2024)

Editors: Aguerre, Carolina; Campbell-Verduyn, Malcolm; Scholte, Jan Aart

Link: https://directory.doabooks.org/handle/20.500.12854/132299

8) Real-Life Decision-Making (2024)

Authors: Danielson, Mats & Ekenberg, Love

Link: https://directory.doabooks.org/handle/20.500.12854/121574

9) Human-Centered AI (2024)

Editors: Régis, Catherine; Denis, Jean-Louis; Axente, Maria Luciana; Kishimoto, Atsuo

Link: https://directory.doabooks.org/handle/20.500.12854/133843

10) Stephanie Dinkins: On Love & Data (2024)

Author: Mitra, Srimoyee (editor)

Link: https://directory.doabooks.org/handle/20.500.12854/131332

11) Knowledge and Digital Technology (2024)

Editors: Glückler, Johannes& Panitz, Robert

Link: https://directory.doabooks.org/handle/20.500.12854/134115

Note: This is part 1 of my list with 11 entries. The second part will follow soon with 10 more open access books on the theme of this newsblog article. Stay tuned, connect and contact me for this and other exiticing stuff on technology, business, and human resource management!

I wish you a nice week!

Stefan Klemens

PS: Do you want to exchange ideas about human resource management, People Analytics, Digital Assessment, or Artificial Intelligence in HRM? Then network, send a message and/or schedule an online meeting. Or the classic way: a phone call.

And: Do you like my work and the content I regularly share? Then I’m happy about a Like or comment on LinkedIn. Thank you! 🙂 🙋‍♂️🌳


Tip: German Data Science Days 2024

Dear visitor!

In some of my recent newsblog articles (read those here and here) I collected 32 HR tech conferences and 13+1 HR & People Analytics Conferences.

And although this seems, with almost 50 conferences, as a collection covering many important data events, it cannot be complete of course (and I did not strive for it neither).

German Data Science Days 2024

Las friday I found another conference that might be of interest for you if you are working with data, statistics, and analytics – thus in the field of data science and in the case of HR data in HR & People Analytics (more or less HR data science, but this term is not used that often):

The German Data Science Days 2024.

Image: German Data Science Society (embedded)

The conference is dated March 7 – 8, 2024, and will take place at the Ludwig-Maximilians-Universität München (LMU Munich). It is organized by the German Data Science Society and has happened since 2018 (the society’s founding year) each year until 2023, which sums up to six conferences by now.

The 2024 conference includes topics and speakers like:

Alexander Haag, ERGO: “Navigating the fusion of AI generations in the insurance industry with ERGO’s AI Factory”

Monica Epple & Christian Pich, Swiss Re: “Navigating the future – How Swiss Re is unlocking data to drive innovation in reinsurance”

Jasmin Weimüller & Dr. Christoph Weisser, BASF: “How is BASF enabling its workforce to use generative AI & Co – Use cases and enablement” [Human Resources!]

Prof. Dr. Florian Stahl, Universität Mannheim: “The BERD data marketpace: A platform connecting companies, universities and research institutions and fostering the collaboration in research and innovation”

Michael Herter, infas 360: “Data Science für Städte und Kommunen”

Murat Topuz, Deutsche Bank: “Fighting financial crime with data analytics”

Karin Immenroth, RTL: “Mit KI in die datengetriebene Zukunft von RTL Deutschland”

Past Conferences 2018 – 2023

And if you look closely and open the pages of the past conferences form 2018 until 2023 you will not only find the programmes of these, but also the presentations (charts, slides) of almost all speakers as a PDF to download (and in one case as a Google Doc presentation).

Here are some sessions of past conferences:

Dr. Fabian Winter, Munich Re: “Data and Analytics at Munich Re” (2023)

Dr. Heide-Gesa Löhlein & Ibrahim Gökce, Telekom: “Personalization in Telecommunications: Mission Impossible?” (2023)

Christian Most, Lufthansa Group: “The Beauty of Complexity: Decision Support in Operations Steering” (2023)

Peter Mayer, Volkswagen AG: “Applying Computer Vision at Volkswagen Group IT” (2022)

Dr. Anca-Oxana Tudoran & Manuel Jockenhöfer, ProSiebenSat.1: “Data Science in the Media” (2022)

Ralph Müller-Eiselt, Bertelsmann Stiftung: „Wir und die Algorithmen – Beziehungsstatus: kompliziert” (2020)

Dr. Sebastian Fischer, Telekom Innovation Laboratories: „Lieber künstlich intelligent als natürlich dumm” (2020)

Dr. Urs Bergmann, Zalando: „Generative models in e-commerce” (2020)

Dominik Koch, Teradata: „The Data Scientists Survival Guide: 10 things that might save your next analytical project” (2020)

Dr. Stephanie Thiemichen, TÜV Süd: „Thinking outside of the box – building reliable and scalable data analytics products” (2020)

Final words

As said above you can find all sessions and many slides of the conferences 2018 bis 2023 on the website of the German Data Science Days. So check it out!

And remember: The next edition takes place in a couple of weeks in March 2024. So you may consider to visit this exiting data science event.

I wish you a pretty start in the new week!

Stefan Klemens

PS: Do you want to exchange ideas about human resource management, People Analytics, Digital Assessment, or Artificial Intelligence in HRM? Then network, send a message and/or schedule an online meeting. Or the classic way: a phone call.

And: Do you like my work and the content I regularly share? Then I’m happy about a Like or comment on LinkedIn. Thank you! 🙂 🙋‍♂️🌳


30 Open Data Sources – Not only for HR & People Analytics

Dear visitor!

Data are at the heart of our society, technology, and organizations. In fact without data, tools to collect and methods to analyze and interpret them our ancestors would not have been able to follow the traces of wild animals for food and to grow plants and breed animals later.

And in the course of this what later was named the First Agricultural Revolution (Neolithic Revolution) cities were build which led to further use of numbers: With taxation as one of the most important (and still is from the state points of view).

Tip: I have collected some important terms around data in German with links, so you might check this page out as well.

The Scientific Revolution

The progress continued slowly but surely and exploded at the end of the Middle Ages and the beginning of the Renaissance with data-based inventions and explorations by people like Nicolaus Copernikus (see this ARTE Documentary about him and his fellow Georg Joachim Rheticus), Johannes Kepler, Tycho Brahe, Galileo Galilei, as well as Isaac Newton and Gottfried Wilhelm Leibniz.

Exact and systematic observation of natural events, the collection of these data over a long period, thinking about these and developing theories, testing these with experiments as well as the exchange and correspondence with other researchers marked the start of the Scientific Revolution and its scientific method which paved the way to the Industrial and Digital Revolution on wich all our knowledge and wealth is based on until today.

Standing on the shoulders of giants”, as Google often quotes, is as true as the onegoing efforts of many people today to solve the micracles of the universe and to answer open questions regarding our earth, our economy, and technological challenges.

Bricks without clay

“Data! data! data!” he shouted impatiently. “I can’t make bricks without clay.”

These words come from the mouth of Sherlock Holmes and the short story “The Adventure of the Copper Bleeches” by Arthur Conan Doyle, which was first published in The Strand Magazine in June 1892 – and which also appeared in the anthology “The Adventures of Sherlock Holmes” in October of the same year.

But let’s the master detective from London himself speak:

Video: Created with D-ID based on an image from Stable Diffusion.

Although the famous detective from London is a fictional character, for his time and even today, his approach is a prime example of how to solve a difficult task, a puzzle and a case: through precise observation, the collection of data, scientific methods and logical reasoning (deduction). In other words, a forerunner of the data scientist!

And every data scientist or people analyst – like Sherlock Holmes – needs data! Data! Data! Fortunately, technological progress since the 1990s with hardware such as computers, chips, the internet, smartphones and increasingly powerful software has led to huge mountains of data from which the valuable “data ore” now needs to be mined (technically: data mining).

Data as ores for knowledge and wisdom

Even if data does not have quite the same material significance as oil or gold, a comparison we read more often, it is still central to making decisions and translating results into action when it comes to the right selection, cleansing of raw data (interesting: similar to ore as a metal or mineral mixture and raw material), analysis and visualization.

Tip 1: See also the data science pyramid (DIKW) with the levels from bottom to top: World → Data → Information → Knowledge → Wisdom; (see e.g. Herter, 2022, “Was ist Data Science?”, p. 25, in Wawrzyniak & Herter (Eds.), Neue Dimensionen in Data Science: Interdisziplinäre Ansätze und Anwendungen aus Wissenschaft und Wirtschaft, Berlin – Offenbach: Wichmann/VDE). Note: Michael Herter is CEO of Bonn based data science company infas 360 GmbH.

Tip 2: A short practical book on data science in German has written Michael Oettinger (2020).
And: For Germany you might take also a look on the work of the German Data Science Society (GDS e. V.) and their event German Data Science Days taking place from March 7-8, 2024 in Munich. See also upcoming events of the Data Science Summit.

Origin of data I: Classification

Anyone who practices data science or people analytics (HR data science) therefore needs data. But where exactly does it come from? What sources are there? And how available is it?

Of course, organizations today primarily generate mass data (big data) as well as smaller amounts of data (small data): Here, data can be differentiated according to who or what it basically comes from and where it originates, such as:

  • Data from nature and agriculture (e.g. weather, soil, animals, plants)
  • Data from technical systems and machines (e.g. power plants, factories, vehicles)
  • Data from the economy, corporate management and the financial sector (macroeconomic figures, key business figures, taxes).

And what I am interested in as an HR data scientist or people analyst: data from people. More precisely: data from people in organizations – i.e. from employees, managers and trainees (HR data).

There are a number of other ways of classifying data, such as raw data, aggregated data or metadata, according to data type or file format or authorizations. However, it is important that such data classification takes place in accordance with the existing guidelines and is checked over time.

This is always personal data under data protection law, as is the case with customer data or patient data, which is subject to special legal protection.

Personal data also includes data that can be used to identify a person with reasonable effort, such as the license plate number, the account number or the personnel number, which are often used in databases as so-called primary and foreign keys.

Origin of data II: Internal sources

But let’s leave these information technology and legal aspects behind and return to the initial question: Where does the data come from?

Because as I said: (HR) data science and people analytics need to develop and implement solutions to HR challenges: Data.

Fortunately, a large amount of data is collected and stored within an organization today, which, together with other internal or external data, is available to the employee or external service provider for analysis.

Well, in the case of data science or people analytics projects, we usually have access to this data – even if it often involves a lot of effort, communication and processing; as well as to the relevant data sources of interest from business and human resources management such as databases, data warehouses or data lakes (or other modern architectures such as data lakehouses or data meshes). The relevant data is often also available as files (flat files) in various formats (e.g. cvs, xls, xml, json).

The development of data systems, the storage and use of data (transformation, extraction) is summarized under the term data engineering, which has led to the profession of data engineer, as the complexity of IT systems and the challenges posed by big data, IT system landscapes, software diversity and cyber security, for example, have grown significantly in the last 10 years.

The data from Human Resource Management (HR data for short) includes, for example, personnel master data and applicant data, wage and salary data, data on sick leave and fluctuation, data on qualifications and further training (e.g. e-learning) or on job satisfaction and employee management.

In the case of internal data from other areas of the company, HR data science and people analytics projects may be interested in communication data, company figures or working hours (e.g. overtime), depending on the issue at hand.

However, there are situations in which we do not have access to company data, but still need it for testing, training or demonstration purposes. What can we do? The solution: Public or open data!

Tip: For a comprehensive overview of these internal data sources and data from third parties (external data), see the short and practical reference book by Steffi Rudel (2021).

Origin of data III: External sources

There are a lot of external sources for data available which allow access of public or open data.

However, while there are many Internet offerings for a lot of data from politics, society, the environment, transport and health, to name but a few, real data on human resource management is very rare for obvious reasons of data protection and company secrecy.

However, there are some real and fictitious HR data sets that can be used for various purposes for data science and data analysis. For example, for practicing and learning, for testing hypotheses or for comparison with your own HR data.

External data from public, general and special sources with data on the labor market, employer ratings, customer satisfaction, demographic characteristics or the industry and market are also used for specific questions in an HR data science or people analytics project.

Image: First page of our list “30 Open Data Sources 2024 – Not only for HR & People Analytics.”

Schorberg Analytics and Stefan Klemens have collected 30 sources of public and open data in a PDF, which also contains links to a number of HR datasets: If you are interested in this collection contact Stefan Klemens via contact form, e-mail or LinkedIn message. [Please connect there and like three of my latest post, if you have not yet, or comment on it. Friends and supporters of Schorberg Analytics and Stefan Klemens get the PDF of course immediately!].


Personalmagazin 3/2024: Künstliche Intelligenz im Human Resource Management

Zeitschriftentipp: Künstliche Intelligenz im Human Resource Management

Lieber Gast!

HRler kennen und schätzen es: Das Personalmagazin von Haufe. In der Ausgabe 3/2024 legt es den Schwerpunkt auf Künstliche Intelligenz – und den “verantwortungsvoller Umgang mit KI-Systemen”.

Cover der Ausgabe 3/2024 vom Personalmagazin (Graphik eingebettet).

Denn gerade bei dem Hype um Generative Artificial Intelligence (GenAI), natürlich auch im HRM, sind einordnende Berichte wichtig:


Eine gute Woche wünsche ich Dir/Ihnen!

Stefan Klemens

PS: Do you want to exchange ideas about human resource management, People Analytics, Digital Assessment, or Artificial Intelligence in HRM? Then network, send a message and/or schedule an online meeting. Or the classic way: a phone call.

And: Do you like my work and the content I regularly share? Then I’m happy about a Like or comment on LinkedIn. Thank you! 🙂 🙋‍♂️🌳


HR Data Science: Salary Prediction at Trivago and StepStone

Dear guest!

On Tuesday evening, 29th of August 2023, I was at the Düsseldorf Data Science Meetup at Trivago, the Online search company for hotels, in their extraordinary headquarters in the Medienhafen of Düsseldorf, my birth city.

I left my office in Solingen early in the afternoon that day since I met with my business friend Dominik Rühl before the event – and walked in the sun from the Düsseldorfer Landtag (state parliament) at the Rhine to our meeting point at UCI cinema near Trivago.

Stefan Klemens with Dominik Rühl at the meetup. Foto: Stefan Klemens

It was good to see Dominik after about five years – And we had a fruitful exchange on our common topics artificial intelligence (AI), recruitment, skills, and digital assessment as well as some private issues. He is now working as a HR & Recruiting Manager at Advance Business Partner GmbH based nearby in the city of Neuss on the other side of the Rhine. The consulting company focuses on mobility services in different areas like recruitment, innovation, and transformation management.

Although the summer and weather this year in Germany is pretty unstable, we enjoyed sitting outside with our drinks at unique brewery bar Eigelstein.

Find out more about the Düsseldorf Data Science Meetup Group with its interests in Data Science, Machine Learning and Python/R, on this website.

Arriving at Trivago

The Trivago building as seen from the north-east of the Medienhafen Düsseldorf. Foto: Stefan Klemens

At 6 pm it was time to walk to nearby Trivago building, finished in 2018. The individual modern styled entrance area and the café behind offers a glimpse on how the interior of the building is decorated (see this article and this article about the New Work culture at Trivago and the architecture of the headquarter´s spaces.)

Surprisingly we, with another guest, were the first participants arriving (ok, it was half our before the official start and talks started even later), but were soon picked up by Gina from Trivago. Together we (and a cart full of pizza in yellow boxes for the data people) were lifted by one of the elevators to the top floor for the location event.

A stunning view to the south-west skyline from the roof terrace reached our eyes, and Dominik, the coming participants, and me enjoyed drinks and pizza before the event started at 7 pm.

Our co-host Aida Orujova gave us a very warm welcome, she introduced the speakers, and broke the ice by asking who is from data science, who is from engineering, and who just there to know more about salaries.

Co-host and moderator of the evening Aida Orujova welcoming the Data Science crowd. Foto: Stefan Klemens (with approval of the her)

First talk: Alexander Fischer, Trivago

Alexander Fischer from Trivago started with his talk about is passion for the programming and statistics software R, and his (and the economists´) “Swiss knife” methodical approach for prediction outcome variables: Linear Regression. He showed how he and his team used this classical algorithm with packages R´s fixest, and PyFixest to predict wage by using the variables education and ability (e.g. intelligence).

In his presentation of the problem in doing that (“The error term is correlated with the dependent variable”) he referred to a recent study using data from 59,000 Swedish men published 2023 by Marc Keuschnigg, Arnout van de Rijt, and Thijs Bol in the European Sociological Review (number 20, pages 1-14), titled “The plateauing of cognitive ability among top earners” (online article published here on January 28, 2023).

Since AB-Testing (or randomized experimental and control group design) is not feasible in the model (sending randomized individuals in one group for example one year more to college) the classical solution in Social Sciences and Psychology are Quasi-Experiments which were first introduced in the literature by standard book “Quasi-Experimentation: Design and Analysis Issues for Field Settings” written by Cook and Campell (1979).

As a solution for not manipulation experimental the years of education as predictor of the wage Alexander used therefore a variable called “distance to college” as a natural differing factor between people regarding their years of education.

The data scientist from Trivago further pointed out in his “The Secret Sauce” slide that taking the role of companies into account in the corresponding regression model, the computation is quite demanding (millions of employees, thousands of companies, 20 years of data) – But he presented of course a solution for it (and that was not Spark!).

At the end with the help of programming language Python and package PyFixest Alexander showed that the prediction of salary can be done, and he answered the questions of the audience.

Second talk: Michael Matuschek & Tim Elfrink, StepStone

In the second talk this evening we learned from Michael Matuschek and Tim Elfrink how StepStone is predicting the salaries of all kinds of jobs for their salary products.

Michael begun the session, and gave an overview about StepStone´s salary products include the Salary Planer, Salary on Listings, and Auto-generated Salary SEO pages.

As a result of a 2020 study and further research before it turned out that salary is for 96 % of the respondents the most important criteria when choosing a job (flexible working ours, career & training opportunities, and corporate culture, reach only 90 % resp. 91 %).

Michael Matuschek with Tim Elfrink from StepStone answering a question from the audience. Foto. Stefan Klemens. Thanks both for their approval of the picture!

Michael told us also about the challenges in prediction salary at StepStone regarding data distribution and features (more white collar jobs and little part-time data for example) and: The gender pay gap, quality assurance, feature engineering, the underlying model and the used algorithm, as well as the metrics (main business KPIs) accuracy and generalisation.

After him Tim Elfrink took the mic and explained the broader infrastructure of the predicting IT system with AWS and the auto deployment of the model. Further subtitles of his presentation were for example: Creating scalable infrastructure and development environment.

A number of questions (and some hints for improving their model) came from the participants, and Michael and Tim were happily answering them.

Closing, socks, and outlook

At 8.30 pm presenter Aida Orujova returned to the stage again and thanked all guests and speakers for being there. As several others I took the chance to talk with some participants (see header picture), before I needed to catch my tram to travel home.

Trivago-Logo in front of the building after sunset. Foto: Stefan Klemens

My second Düsseldorf Data Science Meetup was another wonderful experience (read about my first here), and the scheduled next event in October 2023 is of course on my list.

Oh, one last thing (we learned this from the apple guy, right?) I did not mention yet. Before the start the participants could grab one, two, or three promotional gifts from Trivago as shown in the picture: One for using your hand to write (still common among a few people I was told), one for storing big data in a small piece of metal, and one to keep your feet between 28 ° C and 33 ° C (surface temperature of the extremities as I learned writing this sentence) when external temperatures fall in later autumn.

Promotional gifts for the participants of the Düsseldorf Data Science Meetup from Trivago. Foto: Stefan Klemens

As I like to test digital and analogue things (I have high scores on openness to experience (see the Big Five Personality Traits) and curiosity which is one of my signature strengths according to the VIA-Model), the usefulness of the trivagonian socks to prevent cold toes needed to be proven also.

Note: If you like to know more about psychological traits and psychometric assessment of these for HR recruiting, selection, and development, then click on my work as a Work Psychologist as presented here: https://www.digitalassessment.de/

I can say that my feet got warmer but the real test of course – and perhaps then like a case study (N = 1) with more treatments like a stepstonian, a sipgatian, and quantopian fabric as well a control (no treatment, that is walking without socks! preparing for that right know!) – will be conducted in colder times which are coming soon to Germany. I will report on it! 😉 And perhaps you wanna join the experiment to lift the “N”, so results will be more valid?

Me testing the Trivago socks: And I am smiling realizing the double meaning of the words and symbols matching the two main areas of the company. Foto: Stefan Klemens

With this of course rather funny ending, I thank very much the organizers and speakers for this evening, and Trivago for hosting the meeting! Will we see us next time on a Düsseldorf Data Science Meetup (or another place if you like)?

Many greeting and all the best to you!

Stefan Klemens

PS: Want to exchange ideas on people analytics, digital assessment or artificial intelligence in HRM? Then network, write a message and/or make an appointment for an online meeting. Or the classic way: phone call.

And: You like my work and the content I regularly share? Then I’m happy about a Like or comment on LinkedIn. Thank you! 🙂 🙋‍♂️🌳