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Riga Pilot Report
December, 2025 • Project deliverable
Skudra, Sabine
There were two citizen science campaigns implemented in the Riga pilot in 2024 and 2025. The first campaign lasted 2 years while the second lasted one year but both were diverse with approaches, resul…
There were two citizen science campaigns implemented in the Riga pilot in 2024 and 2025. The first campaign lasted 2 years while the second lasted one year but both were diverse with approaches, results and lessons learned. Both campaigns yielded important experiences to develop further within the Urban ReLeaf project and in upcoming projects implemented in the Riga planning region.
Air Quality Campaign (Riga, 2024–2025) - "Adopt a sensor"
Purpose: Assess the role of greenspaces in reducing PM2.5 exposure and improving public health.Approach:
Installed 20 low-cost PurpleAir sensors across diverse sites (urban green, near-green, near-traffic, traffic) to complement two regulatory stations.
Engaged citizens via “Adopt a Sensor” open call; strong volunteer response (40 applications).
Developed partnerships with municipal institutions, educational organisations, and non-governmental organisations (NGOs).
Initiated cooperation with Riga Digital Agency to create a citywide data visualisation platform.
Results:
Over 87,000 mean observation hours collected in Year 1; indicative trends align with official monitoring.
Urban greenspaces consistently linked to lower PM2.5 exposure, though not fully isolated from traffic/domestic emissions.
No exceedances of EU short-term PM2.5 thresholds; annual averages suggest localized hotspots.
Data supports arguments for low-emission zones and greening strategies in Riga.
Lessons Learned:
Citizen engagement exceeded expectations; volunteers remain committed beyond campaign.
Clear communication and technical readiness are critical for sustained participation.
Expansion of sensor network and integration into city planning recommended.
Heat Stress Campaign (Riga, Summer 2025) "Step towards greener city"
Purpose: Raise awareness of urban heat islands and the role of greenery; involve citizens in temperature and relative humidity (TRH) data collection.Approach:
Distributed 54 TRH sensors; collected ~49,000 paired observations via EcoPulse app.
Organised educational events and thematic walks to strengthen community engagement.
Collaborated with related projects (LIFE LATESTadapt, SATSDIFACTION) for knowledge exchange.
Findings:
Recorded temperatures ranged from 9.6°C to 40.2°C; mobile TRH sensors showed ~4.4°C higher readings than reference stations, confirming heat island effects.
Data highlights cooling benefits of green spaces and need for improved urban design.
Technical challenges with app usability affected participation; WhatsApp community support proved essential.
Lessons Learned:
Citizen science is effective for awareness and data collection but requires user-friendly technology.
Future campaigns should improve technical reliability, maintain strong communication, and explore gamification for motivation.
Strategic Impact and Next Steps
Urban ReLeaf has strengthened Riga’s capacity for data-driven environmental governance.
Ongoing development of a citywide data visualisation platform will enable broader use of collected data.
Recommendations include expanding sensor networks, improving citizen engagement strategies, and leveraging findings for greening plans and low-emission zones.
Continued collaboration with EU projects and local stakeholders will ensure alignment with climate neutrality goals.
Citizen ScienceCommunity EngagementUrban EnvironmentAir QualityUrban Heat Island
This dataset provides a global time series of land cover indicators at 100 m spatial resolution for the period 2000–2024. It is part of a global ensemble of eight land cover indicators deri…
This dataset provides a global time series of land cover indicators at 100 m spatial resolution for the period 2000–2024. It is part of a global ensemble of eight land cover indicators derived from two global land cover products, namely GLAD GLCLUC and GLC_FCS30D.The dataset was generated by combining the source land cover products through temporal interpolation, weighted averaging, and class overwriting. The native resolution of the source products is 30 m. The resulting data were resampled to 100 m using average resampling and are provided as COGs.The eight land cover indicators included in the overall dataset are:tfshortveg, tftrees, wtshortveg, wttrees, water, cropland, snowice, builtupFurther details on the methodology, source datasets, processing workflow, and data structure are available in the associated GitHub repository.This Zenodo record contains the terra firma tree cover height (tftreeheight) indicator. The indicator represents dominant tree height on dry ground, from ~3 m to >25 m.Value type: Height (m)Years covered: 2021-2024
land coverland cover changeremote sensingGLAD GLCLUGLC_FCS30D
This dataset provides a global time series of land cover indicators at 100 m spatial resolution for the period 2000–2024. It is part of a global ensemble of eight land cover indicators deri…
This dataset provides a global time series of land cover indicators at 100 m spatial resolution for the period 2000–2024. It is part of a global ensemble of eight land cover indicators derived from two global land cover products, namely GLAD GLCLUC and GLC_FCS30D.The dataset was generated by combining the source land cover products through temporal interpolation, weighted averaging, and class overwriting. The native resolution of the source products is 30 m. The resulting data were resampled to 100 m using average resampling and are provided as COGs.The eight land cover indicators included in the overall dataset are:tfshortveg, tftrees, wtshortveg, wttrees, water, cropland, snowice, builtupFurther details on the methodology, source datasets, processing workflow, and data structure are available in the associated GitHub repository.This Zenodo record contains the terra firma short vegetation cover (tfshortveg) indicator. The indicator represents cover of short vegetation on dry ground, from true desert (~3%) to dense short vegetation (100%).Value type: Percentage (0-100%)Years covered: 2000-2003
land coverland cover changeremote sensingGLAD GLCLUGLC_FCS30D
This dataset provides a global time series of land cover indicators at 100 m spatial resolution for the period 2000–2024. It is part of a global ensemble of eight land cover indicators deri…
This dataset provides a global time series of land cover indicators at 100 m spatial resolution for the period 2000–2024. It is part of a global ensemble of eight land cover indicators derived from two global land cover products, namely GLAD GLCLUC and GLC_FCS30D.The dataset was generated by combining the source land cover products through temporal interpolation, weighted averaging, and class overwriting. The native resolution of the source products is 30 m. The resulting data were resampled to 100 m using average resampling and are provided as COGs.The eight land cover indicators included in the overall dataset are:tfshortveg, tftrees, wtshortveg, wttrees, water, cropland, snowice, builtupFurther details on the methodology, source datasets, processing workflow, and data structure are available in the associated GitHub repository.This Zenodo record contains the built-up (builtup) indicator. The indicator represents likelihood of man-made infrastructure, commercial, or residential surfaces.Value type: Percentage (0-100%)Years covered: 2020-2024
land coverland cover changeremote sensingGLAD GLCLUGLC_FCS30D
This dataset provides a global time series of land cover indicators at 100 m spatial resolution for the period 2000–2024. It is part of a global ensemble of eight land cover indicators deri…
This dataset provides a global time series of land cover indicators at 100 m spatial resolution for the period 2000–2024. It is part of a global ensemble of eight land cover indicators derived from two global land cover products, namely GLAD GLCLUC and GLC_FCS30D.The dataset was generated by combining the source land cover products through temporal interpolation, weighted averaging, and class overwriting. The native resolution of the source products is 30 m. The resulting data were resampled to 100 m using average resampling and are provided as COGs.The eight land cover indicators included in the overall dataset are:tfshortveg, tftrees, wtshortveg, wttrees, water, cropland, snowice, builtupFurther details on the methodology, source datasets, processing workflow, and data structure are available in the associated GitHub repository.This Zenodo record contains the built-up (builtup) indicator. The indicator represents likelihood of man-made infrastructure, commercial, or residential surfaces.Value type: Percentage (0-100%)Years covered: 2000-2019
land coverland cover changeremote sensingGLAD GLCLUGLC_FCS30D
This dataset provides a global time series of land cover indicators at 100 m spatial resolution for the period 2000–2024. It is part of a global ensemble of eight land cover indicators deri…
This dataset provides a global time series of land cover indicators at 100 m spatial resolution for the period 2000–2024. It is part of a global ensemble of eight land cover indicators derived from two global land cover products, namely GLAD GLCLUC and GLC_FCS30D.The dataset was generated by combining the source land cover products through temporal interpolation, weighted averaging, and class overwriting. The native resolution of the source products is 30 m. The resulting data were resampled to 100 m using average resampling and are provided as COGs.The eight land cover indicators included in the overall dataset are:tfshortveg, tftrees, wtshortveg, wttrees, water, cropland, snowice, builtupFurther details on the methodology, source datasets, processing workflow, and data structure are available in the associated GitHub repository.This Zenodo record contains the snow and ice (snowice) indicator. The indicator represents likelihood of perennial glacier/snow cover.Value type: Percentage (0-100%)Years covered: 2000-2024
land coverland cover changeremote sensingGLAD GLCLUGLC_FCS30D
Data supporting: Exploring the historical biogeography of diving beetles (Coleoptera: Adephaga: Dytiscidae) and the determinants of their biogeographic range size
September, 2026 • Dataset
Ferreira, Jules, Condamine, Fabien L., Toussaint, Emmanuel F. A.
Supporting Information
For all the following Supporting Information, the code for the defined biogeographic regions is as follows: A = Neotropics; B = Nearctic; C = Palearctic; D = Pacific Ocean; E = …
Supporting Information
For all the following Supporting Information, the code for the defined biogeographic regions is as follows: A = Neotropics; B = Nearctic; C = Palearctic; D = Pacific Ocean; E = Afrotropics; F = Indian Ocean; G = Oriental region; H = Australasia; I = Antarctic region.
File S1. Molecular matrix used in this study, as well as MrBayes scripts and resulting trees for all analyses performed.
File S2. Phylogenetic trees pruned, resulting in 157 tips trees.
File S3. Script used to perform RevBayes diversification rate analyses, as well as log files resulting from it for each topology.
File S4. Data and R script used to perform phylogenetic comparative analyses using the R package ‘SLOUCH’.
File S5. Results of the historical biogeography analyses using the R package ‘BioGeoBEARS’ under .Rdata format.
Figure S1. Results of the biogeographic analysis conducted with the ‘AZ’ topology (179 tips and including the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S2. Results of the biogeographic analysis conducted with the ‘BZ’ topology (179 tips and including the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S3. Results of the biogeographic analysis conducted with the ‘CZ’ topology (179 tips and including the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S4. Results of the biogeographic analysis conducted with the ‘EZ’ topology (179 tips and including the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S5. Results of the biogeographic analysis conducted with the ‘FZ’ topology (179 tips and including the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S6. Results of the biogeographic analysis conducted with the ‘GZ’ topology (179 tips and including the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S7. Results of the biogeographic analysis conducted with the ‘AZ’ topology (179 tips and excluding the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S8. Results of the biogeographic analysis conducted with the ‘BZ’ topology (179 tips and excluding the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S9. Results of the biogeographic analysis conducted with the ‘CZ’ topology (179 tips and excluding the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S10. Results of the biogeographic analysis conducted with the ‘EZ’ topology (179 tips and excluding the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S11. Results of the biogeographic analysis conducted with the ‘FZ’ topology (179 tips and excluding the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S12. Results of the biogeographic analysis conducted with the ‘GZ’ topology (179 tips and excluding the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S13. Results of the biogeographic analysis conducted with the ‘AZ’ topology (157 tips and including the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S14. Results of the biogeographic analysis conducted with the ‘BZ’ topology (157 tips and including the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S15. Results of the biogeographic analysis conducted with the ‘CZ’ topology (157 tips and including the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S16. Results of the biogeographic analysis conducted with the ‘EZ’ topology (157 tips and including the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S17. Results of the biogeographic analysis conducted with the ‘FZ’ topology (157 tips and including the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S18. Results of the biogeographic analysis conducted with the ‘GZ’ topology (157 tips and including the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S19. Results of the biogeographic analysis conducted with the ‘AZ’ topology (157 tips and excluding the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S20. Results of the biogeographic analysis conducted with the ‘BZ’ topology (157 tips and excluding the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S21. Results of the biogeographic analysis conducted with the ‘CZ’ topology (157 tips and excluding the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S22. Results of the biogeographic analysis conducted with the ‘EZ’ topology (157 tips and excluding the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S23. Results of the biogeographic analysis conducted with the ‘FZ’ topology (157 tips and excluding the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Figure S24. Results of the biogeographic analysis conducted with the ‘GZ’ topology (157 tips and excluding the Antarctic region) with ‘BioGeoBEARS’, either given as the most likely ancestral range or with pie charts at nodes showing relative probabilities of ancestral range.
Table S1. Dataset of taxa used in phylogenetic analyses, with GenBank accession numbers for each available marker and specimen, and voucher information.
Table S2. Assignment of biogeographic ranges to each taxon of Dytiscidae represented in the biogeographic analyses, including or excluding the Antarctic region, for analyses with either 179 tips or 157 tips.
Table S3. Adjacency matrices for the ‘BioGeoBEARS’ stratified biogeographic analyses, divided into seven time slices and including or excluding the Antarctic region.
Table S4. Manual multiplier dispersal rates matrices for the ‘BioGeoBEARS’ stratified biogeographic analyses, divided into seven time slices and including or excluding the Antarctic region.
Table S5. Allowed area matrices for the ‘BioGeoBEARS’ stratified biogeographic analyses, divided into seven time slices and including or excluding the Antarctic region.
Table S6. Results of the Biogeographical Stochastic Mapping (BSM) for the ‘EZ’ topology with 179 tips and including or excluding the Antarctic region.
Table S7. Divergence time estimates and most likely ancestral range for Dytiscidae and their subclades for the analyses under the topologies 'AZ', 'EZ', 'BZ', 'FZ', 'CZ', ‘GZ’, with 179 tips, 157 tips, and including or excluding the Antarctic region. The results for the analyses with 179 tips and including Antarctica for the topologies 'AZ' and 'EZ' are presented in Table 1.
Table S8. Results of biogeographic range size regressed on habitat type with the R package 'SLOUCH', with a coding strategy following the literature.
Table S9. Results of biogeographic range size regressed on habitat type with the R package 'SLOUCH', with a coding strategy using the dominant habitat for some genera with mixed habitat types.
Table S10. Results of biogeographic range size regressed on niche breadth with the R package 'SLOUCH', with a coding strategy following the literature.
Table S11. Results of biogeographic range size regressed on niche breadth with the R package 'SLOUCH', with a coding strategy using the dominant habitat for some genera with mixed habitat types.
Table S12. Results of biogeographic range size regressed on mean body size of genera with the R package 'SLOUCH'.
Table S13. Results of biogeographic range size regressed on diversification rates using the formula from Magallón and Sanderson (2001), that is ln[(number of species) – ln(2)] / clade age, with the R package 'SLOUCH'.
Table S14. Results of biogeographic range size regressed on diversification rates using the RevBayes framework, with the R package 'SLOUCH'.
This dataset provides a global time series of land cover indicators at 100 m spatial resolution for the period 2000–2024. It is part of a global ensemble of eight land cover indicators deri…
This dataset provides a global time series of land cover indicators at 100 m spatial resolution for the period 2000–2024. It is part of a global ensemble of eight land cover indicators derived from two global land cover products, namely GLAD GLCLUC and GLC_FCS30D.The dataset was generated by combining the source land cover products through temporal interpolation, weighted averaging, and class overwriting. The native resolution of the source products is 30 m. The resulting data were resampled to 100 m using average resampling and are provided as COGs.The eight land cover indicators included in the overall dataset are:tfshortveg, tftrees, wtshortveg, wttrees, water, cropland, snowice, builtupFurther details on the methodology, source datasets, processing workflow, and data structure are available in the associated GitHub repository.This Zenodo record contains the cropland (cropland) indicator. The indicator represents likelihood of annual/perennial herbaceous crop production.Value type: Percentage (0-100%)Years covered: 2023-2024
land coverland cover changeremote sensingGLAD GLCLUGLC_FCS30D
This dataset provides a global time series of land cover indicators at 100 m spatial resolution for the period 2000–2024. It is part of a global ensemble of eight land cover indicators deri…
This dataset provides a global time series of land cover indicators at 100 m spatial resolution for the period 2000–2024. It is part of a global ensemble of eight land cover indicators derived from two global land cover products, namely GLAD GLCLUC and GLC_FCS30D.The dataset was generated by combining the source land cover products through temporal interpolation, weighted averaging, and class overwriting. The native resolution of the source products is 30 m. The resulting data were resampled to 100 m using average resampling and are provided as COGs.The eight land cover indicators included in the overall dataset are:tfshortveg, tftrees, wtshortveg, wttrees, water, cropland, snowice, builtupFurther details on the methodology, source datasets, processing workflow, and data structure are available in the associated GitHub repository.This Zenodo record contains the cropland (cropland) indicator. The indicator represents likelihood of annual/perennial herbaceous crop production.Value type: Percentage (0-100%)Years covered: 2012-2022
land coverland cover changeremote sensingGLAD GLCLUGLC_FCS30D
There is an increasing interest in upgrading the EModel, a parametric tool for speech quality estimation, to the wideband and super-wideband contexts. The
Contemporary models of Unmanned Aerial Vehicles (UAVs) are largely developed using simulators. In a typical scheme, a flight simulator is dovetailed with a
Undertaking engineering research can be compounding for beginning graduate students and thwarting even for seasoned researchers. With a wealth of academic
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