Competition Data Observatory

Reprex: Big Data For All

Mon, 07 Nov 2022 18:00:00 +0000

Reprex is the Hague-based impact startup developing decentralized, modern, web 3.0-compatible data observatories. Our mission is to fulfill parts of the SDG 16 and 17 goals: based on the open collaboration method of open-source software development and open knowledge management, we would like to enable impact makers to contribute to other SDG goals by making AI and big data work for them.

Table of Contents

Watch Our 2-min Introduction

⚙️/ Subtitles/ 🇳🇱 🇬🇧 🇧🇦 🇨🇿 🇭🇺 🇩🇪 🇱🇹 🇫🇷 🇸🇰 🇪🇸 🇹🇷 + Catalan.

How To Vote?

Go to Cast your vote for The Hague Innovators challenge 2022! and choose Reprex :)

Make sure you select Reprex and write in your email (it is safe here.) You need to tick `✅ I’m not a robot’ to be able to select companies. Further instructions 🇬🇧 here 🇭🇺 magyarul itt.

What do we do in the music industry and in the music cultural sector?

Our data coverage already includes some of the least developed countries.
The Digital Music Observatory already has more than 16 institutional users, and more than 20 external curators.
With the help of a significant 3-million-euro grant to develop this into a permanent European Music Observatory vying for official recognition of the EU.
Has a track record to solve complex problems, e.g., valuing and pricing music, providing evidence on piracy (contributing to SDG 8—improving decent working conditions for precarious creative workers), predicting audiences, and finding algorithmic biases against small-country artists and womxn (SDG 4, 5 - AI, metadata, data problems in music education and bias against womxn)
In a partnership with the Hague and the Dutch music institutions in our city, we could bring about 20 jobs to the town and make it one of the most important knowledge hubs on music worldwide

Product

Our data observatories (platform products) cover our R&D and platform costs while giving us access to an expanding range of prime clients. We use 21-st century open-source data engineering solutions, a decentralized data governance method, and web 3.0 technologies to avoid conflicts of interest and prevent the data Sisyphus of error-prone human data wrangling. There is little competition on this service level (there are about 60 UN/EU/OECD recognized data observatories, and almost all of them are managed by a different operator.) This layer is already monetized, and we have proven success. Our unique advantage is a combination of legal and technological skills: understanding legally open data, web 3.0, and data modeling, and the ability to participate in the open-source statistical /scientific software creator community.
We create open-source software applications that fuel our data observatories with unprocessed, open, linked data. We create software for the R statistical environment, which is used in both official statistics and in many business and academic organizations. The production of R software components is a competitive field, but we believe that our position is strong: the vast majority of R packages are lightly or not at all serviced because of the lack of financing.

Reprex produces open-source scientific software, and various collaborative data engineering infrastructures to get legally open governmental data and open science data in a timely, usable format to ecological researchers, and ecotech innovators.

We provide bespoke analytics solutions to our institutional partners in our data observatories. Such bespoke solutions iterate over our existing software components, helping us design better applications within an ever-expanding ecosystem. Providing tailored data-science services would require a large organization without a clear focus. We provide these services on an ad-hoc basis only among institutional partners and users of our data observatories. In these circles, which are often prime clients, we face little or no competition because we are trusted partners and data and solution providers. This is a key to our revenue and market growth.
We develop high-value software-as-service applications that leverage our data observatory assets and our software solution into a novel, commercially valuable uses. Our applications are built around our family of open-source software and generalize our bespoke analytics solutions. We are in a late prototype phase where we already have some revenue and are trying to prepare for scaling up at the correct price with three of our applications. All of our applications are entering into highly competitive market segments. We are building on our ‘unfair’ advantage that we are bundling our solutions with data that is not accessible to competitors, and we can test them in the protected ecosystems of our observatories.

Plans in The Hague?

Our message is simple: doing business and doing good for the city of the Hague means a vote for Reprex. We would like to win the Hague Innovators Challenge in 2022 because we believe we could multiply our growth in partnership with the Hague. We have a significant budget to develop our observatories, and our company is already located in the Hague, in Apollo 14—but most of our team members, not to mention the observatory’s non-data personnel are not based in our beautiful and smart city. The observatories are important platforms for our growth, and they could create a lot more jobs and impact in the city than in our startup company. Should we win the prize, we would spend the 25,000 euros on one thing: to develop our observatories into a real public-private partnership in the Hague, with a permanent office in Apollo 14 or the Hague Humanity Hub.

Reprex’s data observatories, particularly the Green Deal Data Observatory are public-private partnerships that foster the collective collection, processing, peer-review, and reuse of novel big data, like BeeSage’s beehive data, and reusable statistical and environmental data. We hope to place the permanent institution of this PPP in the Hague, which is already the World’s Smartest City, and which wants to remain a global centre of excellence of peace, justice, and sustainability in the era of big data and AI.

Identifying Roadblocks to Net Zero Legislation

Tue, 16 Mar 2021 00:00:00 +0000

In our use case we are merging data about Europe’s coal regions, harmonized surveys about the acceptance of climate policies, and socio-economic data. While the work starts out from existing European research, our retroharmonize survey harmonization solution, our regions sub-national boundary harmonization solution and iotables allows us to connect open data and open knowledge from other coal regions of the world, for example, from the Appalachian economy.

Policy Context

The Just Transition Platform aims to assist EU countries and regions to unlock the support available through the Just Transition Mechanism. It builds on and expands the work of the existing Initiative for Coal Regions in Transition, which already supports fossil fuel producing regions across the EU in achieving a just transition through tailored, needs-oriented assistance and capacity-building.

The Initiative has a secretariat that is co-run by Ecorys, Climate Strategies, ICLEI Europe, and the Wuppertal Institute for Climate. While the initiative is an EU project, it cooperates with other similar initiatives, for example, with the Coalfield Development social enterprise in the Appalachian economy.

Data Sources

Coal regions: Our starting point is the EU coal regions: opportunities and challenges ahead publication Joint Research Centre (JRC), the European Commission’s science and knowledge service. This publication maps Europe’s coal dependent energy and transport infrastructure, and regions that depend on coal-related jobs.
Harmonized Survey Data: The dataset of the Eurobarometer 91.3 (April 2019) harmonized survey. Our transition policy variable is the four-level agreement with the statement More public financial support should be given to the transition to clean energies even if it means subsidies to fossil fuels should be reduced (EN) and Davantage de soutien financier public devrait être donné à la transition vers les énergies propres même si cela signifie que les subventions aux énergies fossiles devraient être réduites (FR) which is then translated to the language use of all participating country.
Environmental Variables: We used data on pm and SO2 polution measured by participating stations in the European Environmental Agency’s monitoring program. The station locations were mapped by Milos to the NUTS sub-national regions.

Exploratory Data Analysis

Our coal-dependency dummy variable is base on the policy document Coal regions in transition.

readRDS(file.path("data", "coal_regions.rds"))

## # A tibble: 253 x 5
##    country_code_is~ region_nuts_nam~ region_nuts_cod~ coal_region is_coal_region
##    <chr>            <fct>            <chr>            <chr>                <dbl>
##  1 BE               Brussels hoofds~ BE10             <NA>                     0
##  2 BE               Liege            BE33             <NA>                     0
##  3 BE               Brabant Wallon   BE31             <NA>                     0
##  4 BE               Antwerpen        BE21             <NA>                     0
##  5 BE               Limburg [BE]     BE22             <NA>                     0
##  6 BE               Oost-Vlaanderen  BE23             <NA>                     0
##  7 BE               Vlaams Brabant   BE24             <NA>                     0
##  8 BE               West-Vlaanderen  BE25             <NA>                     0
##  9 BE               Hainaut          BE32             <NA>                     0
## 10 BE               Namur            BE35             <NA>                     0
## # ... with 243 more rows

Our exploratory data analysis shows that respondent in 2019, agreement with the policy measure significantly differed among EU member states and regions.

transition_policy <- eb19_raw %>%
  rowid_to_column() %>%
  mutate ( transition_policy: normalize_text(transition_policy)) %>%
  fastDummies::dummy_cols(select_columns: 'transition_policy') %>%
  mutate ( transition_policy_agree: case_when(
    transition_policy_totally_agree + transition_policy_tend_to_agree > 0 ~ 1, 
    TRUE ~ 0
  )) %>%
  mutate ( transition_policy_disagree: case_when(
    transition_policy_totally_disagree + transition_policy_tend_to_disagree > 0 ~ 1, 
    TRUE ~ 0
  )) 

eb19_df  <- transition_policy %>% 
  left_join ( air_pollutants, by: 'region_nuts_codes' ) %>%
  mutate ( is_poland: ifelse ( country_code == "PL", 1, 0))

Preliminary Results

Significantly more people agree where

there are more polutants
who are younger
where people are more educated

Significantly less people agree

in rural areas
where more people are older
where more people are less educated
in less polluted areas
in coal regions

A simple model run:

c("transition_policy_totally_agree" , "pm10", "so2", "age_exact", "is_highly_educated" , "is_rural")

## [1] "transition_policy_totally_agree" "pm10"                           
## [3] "so2"                             "age_exact"                      
## [5] "is_highly_educated"              "is_rural"

summary( glm ( transition_policy_totally_agree ~ pm10 + so2 + 
                 age_exact +
                 is_highly_educated + is_rural + is_coal_region +
                 country_code, 
               data: eb19_df, 
               family: binomial ))

## 
## Call:
## glm(formula: transition_policy_totally_agree ~ pm10 + so2 + 
##     age_exact + is_highly_educated + is_rural + is_coal_region + 
##     country_code, family: binomial, data: eb19_df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.7690  -1.0253  -0.8165   1.2264   1.9085  
## 
## Coefficients:
##                      Estimate Std. Error z value Pr(>|z|)    
## (Intercept)        -0.1975096  0.0921551  -2.143 0.032095 *  
## pm10                0.0068505  0.0017445   3.927 8.60e-05 ***
## so2                 0.1381994  0.0405867   3.405 0.000662 ***
## age_exact          -0.0075018  0.0007873  -9.529  < 2e-16 ***
## is_highly_educated  0.2953905  0.0311127   9.494  < 2e-16 ***
## is_rural           -0.1277983  0.0313321  -4.079 4.53e-05 ***
## is_coal_region     -0.2624005  0.0640233  -4.099 4.16e-05 ***
## country_codeBE     -0.3290891  0.0916117  -3.592 0.000328 ***
## country_codeBG     -0.6470116  0.1125114  -5.751 8.89e-09 ***
## country_codeCY      0.8471483  0.1273306   6.653 2.87e-11 ***
## country_codeCZ     -0.5754008  0.0965974  -5.957 2.57e-09 ***
## country_codeDE      0.0106430  0.0856322   0.124 0.901088    
## country_codeDK      0.0577724  0.0925391   0.624 0.532429    
## country_codeEE     -0.8041188  0.0989047  -8.130 4.28e-16 ***
## country_codeES      1.1266903  0.0941495  11.967  < 2e-16 ***
## country_codeFI     -0.2617501  0.0946837  -2.764 0.005702 ** 
## country_codeFR      0.0130239  0.1639339   0.079 0.936678    
## country_codeGB      0.2454631  0.0891845   2.752 0.005918 ** 
## country_codeGR      0.2169278  0.1209199   1.794 0.072816 .  
## country_codeHR     -0.1632727  0.1001563  -1.630 0.103064    
## country_codeHU      0.5779928  0.1020987   5.661 1.50e-08 ***
## country_codeIT     -0.1427249  0.0940144  -1.518 0.128985    
## country_codeLU     -0.3111627  0.1140426  -2.728 0.006363 ** 
## country_codeLV     -0.6246590  0.0963526  -6.483 8.99e-11 ***
## country_codeMT      0.3303363  0.1228611   2.689 0.007173 ** 
## country_codeNL      0.1707080  0.0902189   1.892 0.058470 .  
## country_codePL     -0.2843198  0.1228657  -2.314 0.020664 *  
## country_codePT      0.1447295  0.0899079   1.610 0.107452    
## country_codeRO     -0.0479674  0.0930433  -0.516 0.606177    
## country_codeSE      0.4865939  0.0922486   5.275 1.33e-07 ***
## country_codeSK     -0.2427307  0.0964652  -2.516 0.011861 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 30568  on 22401  degrees of freedom
## Residual deviance: 29313  on 22371  degrees of freedom
##   (5253 observations deleted due to missingness)
## AIC: 29375
## 
## Number of Fisher Scoring iterations: 4

summary( glm ( transition_policy_agree ~ pm10 + so2 + age_exact +
                 is_highly_educated + is_rural, 
               data: eb19_df, 
               family: binomial ))

## 
## Call:
## glm(formula: transition_policy_agree ~ pm10 + so2 + age_exact + 
##     is_highly_educated + is_rural, family: binomial, data: eb19_df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1970   0.5035   0.5803   0.6495   0.8465  
## 
## Coefficients:
##                     Estimate Std. Error z value Pr(>|z|)    
## (Intercept)         1.807823   0.079297  22.798  < 2e-16 ***
## pm10                0.005092   0.001239   4.108 3.99e-05 ***
## so2                 0.003274   0.051410   0.064  0.94922    
## age_exact          -0.009781   0.000988  -9.900  < 2e-16 ***
## is_highly_educated  0.396743   0.039735   9.985  < 2e-16 ***
## is_rural           -0.107448   0.037953  -2.831  0.00464 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 20488  on 22401  degrees of freedom
## Residual deviance: 20250  on 22396  degrees of freedom
##   (5253 observations deleted due to missingness)
## AIC: 20262
## 
## Number of Fisher Scoring iterations: 4

Next Steps

After careful documentation, we will very soon publish all the processed, clean datasets on the EU Zenodo repository with clear digital object identification and versioning.
We will seek contact with the Secretariat of the Initiative for Coal Regions in Transition to process all the data annexes in the EU coal regions: opportunities and challenges ahead report.
With our volunteers we want to include coal regions from the United States, Latin America, Australia, Africa first – because we have harmonized survey results – and gradually add the rest of the world.
We will ask political scientists and policy researchers to interpret our findings.