This dataset contains qualitative survey data of the reasons/motivation of citizens participating in open data hackathons and its analysis

This dataset contains the materials used in the session "Care to Share? Investigating Open Science practices adoption among researchers: a hackathon" presented at the Dutch National Open Science Festival on 22nd October 2024.

The data files are derived from: Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686 ad contains two additional fields (Dimensions_Country and Dimensions_FoR) from Dimensions obtained on 15 October 2024, from Digital Science’s Dimensions platform, available at https://app.dimensions.ai.

File list:

PLOS-Dataset-for-Hackathon.xlsx

Data pertaining to the PLOS corpus of articles derived from Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686 with additional data from Dimensions.ai.

Comparator-Dataset-for-Hackathon.xlsx

Data pertaining to the Comparator corpus of articles derived from Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686 with additional data from Dimensions.ai.

Care to share resource sheet.pdf

Document outlining the questions to be investigated during the hackathon as well as key information about the dataset.

OSI-Column-Descriptions_v3_Dec23.pdf
This file is taken from Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686. It describes the fields used in the two data files with the exception of Dimensions_Country and Dimensions_FoR. Descriptions for these are listed in the README tabs of the data files.

The dataset contains the crosstabulation of roles and reasons of citizens who engaged in 11 open agricultural data hackathons (FarmHack) held from 2016 until 2018 (https://doi.org/10.4121/uuid:879be853-ba9d-463d-a2db-51a076e9ce6e)

Presentation by Bruce Haupt on May 14, 2013 to the NetSquared Houston organization. Includes additional project ideas (snapshots) and links to projects from other Cities.

Dataset organized by the Open-Earth-Monitor (OEMC) project within the context of Hackathon 2023.

The dataset contains monthly mean FAPAR values aggregated by each ground station. FAPAR represents the fraction of the incoming (photosynthetic active) radiation that is absorbed by vegetation, and is given in the range 0-1. It is a measure of vegetation health and ecosystem functioning, and a key parameter in light use efficiency models that model primary productivity.

For each monthly FAPAR value, a set of covariates / features were extracted from 32 raster spatial layers, including including satellite (spectral bands and indices) and temperature images (land surface temperature), climate images (precipitation) and digital terrain model (slope and elevation). The features are organized by columns, unique data points in time are identified by the sample_id column, and data points points belonging to the same location are identified by station_number.

Column names:

sample_id: unique identifier of datapoint

station: ground station number

fapar: monthly mean FAPAR

month: month of measurement

modis_{..}: NDVI, EVI, reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared) based on MOD13Q1

modis_lst_day_p{..}: Land surface temperatures daytime of percentiles 5th, 50th and 95th based on MOD11A2

modis_lst_night_p{..}: Land surface temperatures nighttime of percentiles 5th, 50th and 95th based on MOD11A2

wv_yearly_p{..}: Water vapour aggregated yearly by percentiles 25th, 50th and 75th based on derived from MCD19A2

wv_monthly_lt_p{..}: Water vapour aggregated long-term monthly by percentiles 25th, 50th and 75th based on MCD19A2

wv_monthly_lt_sd: Water vapour aggregated long-term monthly standard deviation based on MCD19A2

wv_monthly_ts_raw: Water vapour monthly time series based on MCD19A2

wv_monthly_ts_smooth: Water vapour monthly time series smoothed using the Whittaker method based on MCD19A2

accum_pr_monthly: Monthly accumulated precipitation based on CHELSA timeseries

dtm_{..}: Several DTM derivatives (Elevation, Slope, aspect (sine, cosine), curvature (up- and downslope), openness (negative, positive), compound topographic index (cti), valley bottom flatness (vbf)) based on MERIT DEM

Files

train.csv: Training set with 3,461 rows and 36 columns, including sample id (sample_id - index column), ground station (station), reference month (month), measured FAPAR (fapar), and 32 features / covariates

test.csv: Test set with 4,939 rows and 34 columns, including sample id (sample_id - index column), ground station (station), reference month (month) and 32 features / covariates

sample_submission.csv: a sample submission file with 4,939 rows and 2 columns, including sample id (sample_id - index column) and measured FAPAR (fapar)

Global GDP data from Nationmaster.

This data was collected by researchers at the University of Toronto under the Research Ethics Board Protocol 44460. The dataset is a transcript of the first 2 hours and 9 minutes of collaborative design communication between four members of a team during a North American hackathon in 2023. The design activities captured in the transcript dataset include team formation, idea generation, idea selection, discussions of team goals, and premature team dissolution. The dataset has six variables (index, timestamp, speaker, speech segment, open code(s), and axial code(s)) and 908 lines of speech.

This repository contains data to be used in the context of the Swiss Open Cultural Data Hackathon of 2016.

This material is available thanks to the DHLAB, the newspaper Le Temps and the Swiss National Library.

It is delivered with a CC-BY 2.0 License (details).

Data consists of OCRed articles from Le Temps newspaper, year 1914:

1. text of the articles in XML format (text sub-folder). One folder per month, one folder per day, one file per article. One article can contain several article sub-entities, which originally correspond to separated article blocks on the newspaper page.

2. text annotated with named entities (entities sub-folder). Named entity recognition and disambiguation was performed by querying several web-services: Open Calais, Dandelion and Alchemy. Named entities (and their attributes) are available as in-line annotations within the XML.

To open the archive: tar -jxvf filename.tar.bz2

Community Robot Hackathon Program Market Outlook

According to our latest research, the global Community Robot Hackathon Program market size reached USD 1.34 billion in 2024, with a robust year-on-year growth trajectory. The market is projected to expand at a CAGR of 14.2% from 2025 to 2033, resulting in an anticipated market value of USD 4.14 billion by 2033. This remarkable growth is primarily fueled by rising interest in collaborative robotics, increasing STEM education initiatives, and a surge in innovation-driven competitions that foster community engagement and technological advancements.

The growth of the Community Robot Hackathon Program market is fundamentally underpinned by the global emphasis on STEM (Science, Technology, Engineering, and Mathematics) education and the need to nurture practical skills among students and professionals alike. Educational institutions and government bodies are increasingly integrating hackathon formats into their curricula and outreach programs, recognizing their efficacy in promoting hands-on learning, problem-solving, and teamwork. Hackathons serve as dynamic platforms for participants to apply theoretical knowledge to real-world challenges, particularly in robotics and automation, which are central to future workforce demands. The proliferation of online and hybrid hackathon models has further democratized access, enabling broader participation from diverse geographies and backgrounds. This inclusivity, coupled with the alignment of hackathon objectives with educational and workforce development goals, is expected to sustain the market’s upward trajectory over the next decade.

Another significant growth factor is the increasing involvement of corporate sponsors and technology companies in the Community Robot Hackathon Program market. Corporations are leveraging hackathons as strategic tools for open innovation, talent scouting, and community engagement. By sponsoring or hosting hackathons, companies gain access to fresh ideas, prototype solutions, and potential recruits who have demonstrated practical skills and creativity under pressure. This symbiotic relationship benefits both the corporate sector and hackathon participants, fostering an ecosystem where innovation is accelerated, and technological solutions are co-created with end-users in mind. Additionally, the growing trend of corporate social responsibility (CSR) initiatives has led companies to support community-driven hackathons that address pressing societal issues through robotics, further expanding the market’s scope and impact.

The advancement of digital infrastructure and the proliferation of accessible robotics development platforms are also driving the Community Robot Hackathon Program market. Open-source hardware and software, coupled with affordable robotics kits, have lowered entry barriers for hackathon organizers and participants. This has enabled a wider range of organizations, including nonprofits and grassroots community groups, to host hackathons tailored to local needs and challenges. The integration of emerging technologies such as artificial intelligence, Internet of Things (IoT), and machine learning into hackathon themes has further broadened the market’s appeal, attracting participants from interdisciplinary backgrounds and fostering cross-sector collaboration. As these technological trends continue to evolve, they are expected to catalyze new opportunities for innovation and market expansion.

From a regional perspective, North America remains the dominant market for Community Robot Hackathon Programs, accounting for over 38% of the global market share in 2024. This is attributed to the strong presence of leading technology companies, well-established educational institutions, and a culture of innovation and competition. However, the Asia Pacific region is emerging as a high-growth market, driven by government investments in education, a burgeoning tech startup ecosystem, and increasing digital literacy across countries such as China, India, and Japan. Europe also holds a significant share, with active participation from both public and private sectors in fostering innovation-driven community programs. The Middle East & Africa and Latin America are gradually catching up, propelled by targeted initiatives to bridge digital divides and promote inclusive technological development.

Program Type Analysis

The Program Type segment of the Community Robot Hackath

Community Robot Hackathon Program Market Outlook

According to our latest research, the global Community Robot Hackathon Program market size reached USD 1.19 billion in 2024, driven by the increasing demand for collaborative robotics innovation and STEM education initiatives worldwide. The market is expected to grow at a robust CAGR of 13.7% from 2025 to 2033, reaching a projected value of USD 3.72 billion by 2033. This growth is primarily fueled by the rising emphasis on open innovation, the proliferation of educational and corporate-sponsored hackathons, and expanding government and NGO support for community-driven technology programs.

One of the primary growth factors for the Community Robot Hackathon Program market is the surging integration of robotics into educational curricula and extracurricular activities. Educational institutions across the globe are increasingly adopting hackathon models to foster hands-on STEM learning, critical thinking, and collaborative problem-solving skills among students. These programs not only bridge the gap between theoretical knowledge and practical application but also create pathways for students to engage with the latest advancements in robotics and artificial intelligence. Additionally, the growing popularity of robotics competitions and hackathons as platforms for talent identification and recruitment has further accelerated market expansion, as both public and private sector stakeholders recognize the long-term benefits of investing in such initiatives.

Another significant driver is the wave of corporate and governmental interest in leveraging community hackathons for innovation acceleration. Corporations are actively sponsoring hackathons to crowdsource novel solutions, engage with emerging talent, and foster a culture of open innovation. These events enable companies to address real-world challenges, enhance product development pipelines, and build stronger connections with the tech-savvy community. Simultaneously, governments and NGOs are supporting hackathon programs as part of broader digital transformation and workforce development strategies. By empowering communities to develop socially impactful robotics solutions, these stakeholders are promoting inclusive growth and addressing pressing societal needs, such as healthcare automation, environmental monitoring, and accessible technology.

The proliferation of digital platforms and hybrid event models has also played a transformative role in expanding the reach and impact of community robot hackathon programs. With the advent of virtual and hybrid hackathons, participation barriers related to geography and logistics are being significantly reduced. This democratization of access has led to a more diverse pool of participants, including professionals, startups, nonprofits, and underserved communities. Moreover, the integration of advanced collaboration tools, cloud-based robotics simulation, and remote mentorship has enhanced the quality and scalability of hackathon experiences. As a result, the market is witnessing a surge in both the number and sophistication of hackathon programs, with organizers continuously innovating to deliver value to participants and sponsors alike.

From a regional perspective, North America continues to dominate the Community Robot Hackathon Program market owing to its strong ecosystem of educational institutions, technology companies, and innovation-focused government policies. The Asia Pacific region is rapidly emerging as a high-growth market, fueled by substantial investments in STEM education, digital infrastructure, and public-private partnerships. Europe also maintains a significant market share, driven by its robust research and development landscape and emphasis on social innovation. Meanwhile, regions such as Latin America and the Middle East & Africa are witnessing increasing adoption, supported by international collaborations and targeted capacity-building initiatives. The regional outlook remains highly dynamic, with cross-border knowledge exchange and global partnerships playing a crucial role in shaping market trends.

This dataset consists of hackathon events details held between March 2018 to July 2019. The data is obtained from multiple community contributed data sources such as Hackathon Kaki, Startup Mamak, FB Events and other FB Communities which MaGIC helped to compile. Majority of the hackathons are recorded but some of it could have been missed. Feel free to submit corrections or missing hackathon data to us by emailing to daren@mymagic.my

Dataset organized by the Open-Earth-Monitor (OEMC) project within the context of Hackathon 2023.

The dataset (both train and test) was produced by stratified sampling of the ground-truth data provided by LUCAS Survey, funded by the European Commission. The target land cover considered level-3 classes from the harmonized legend, resulting in 72 classes distributed over 5 years (2006, 2009, 2012, 2015, 2018):

All samples were overlaid with 416 raster spatial layers, including satellite (spectral bands and indices) and temperature images (land surface temperature), climate images (precipitation, air temperature), accessibility and distance maps (highways, water bodies, burned areas), digital terrain model (slope and elevation) and other existing maps (population count and snow covering). The result values were organized in columns, one for each spatial layers, which combined represent the feature space available for ML modeling.

Column names:

The columns are formed by six metadata fields separated by _:

Example: red_landsat.glad.ard_p50_30m_jun25_sep12

Metadata fields:

F1 - Variable name: red

F2 - Variable procedure including product name: landsat.glad.ard

F3 - Position in the probability distribution: p50

F4 - Spatial resolution: 30m

F5 - Start date: jun25

F6 - End date: sep12

Column description:

All the columns can be aggregated in six thematic groups according to F1 and F2:

Satellite images (spectral reflectance & vegetation indices):

blue_landsat.glad.ard_{..}: Quarterly time-series of Landsat blue band (Witjes et al., 2023)

blue_mod13q1_{..}: Monthly time-series of MOD13Q1 blue band (EarthData)

evi_mod13q1.stl.trend.ols.alpha_{..}: Alpha coefficient / intercept (derived by OLS) over the deseasonalized monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (EarthData)

evi_mod13q1.stl.trend.ols.beta_{..}: Beta coefficient / trend (derived by OLS) over the deseasonalized monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (EarthData)

evi_mod13q1.stl.trend_{..}: Deseasonalized monthly time-series (trend component of STL) for MOD13Q1 Enhanced Vegetation Index (EVI) index (EarthData)

evi_mod13q1_{..}: Monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (EarthData)

green_landsat.glad.ard_{..}: Quarterly time-series of Landsat green band (Witjes et al., 2023)

mir_mod13q1_{..}: Monthly time-series of MOD13Q1 mid-infrared band (EarthData)

ndvi_mod13q1_{..}: Monthly time-series of MOD13Q1 normalized vegetation index (NDVI) (EarthData)

nir_landsat.glad.ard_{..}: Quarterly time-series of Landsat near-infrared band (Witjes et al., 2023)

nir_mod13q1_{..}: Monthly time-series of MOD13Q1 near-infrared band (EarthData)

red_landsat.glad.ard_{..}: Quarterly time-series of Landsat red band (Witjes et al., 2023)

red_mod13q1_{..}: Monthly time-series of MOD13Q1 red band (EarthData)

swir1_landsat.glad.ard_{..}: Quarterly time-series of Landsat short-wave infrared-1 band (Witjes et al., 2023)

swir2_landsat.glad.ard_{..}: Quarterly time-series of Landsat short-wave infrared-1 band (Witjes et al., 2023)

Temperature images:

lst_mod11a2.daytime_{..}: Monthly time-series of MOD13Q1 day time land surface temperature (EarthData)

lst_mod11a2.daytime.{month}_{..}: Long-term monthly aggregation (2000—2022) for MOD13Q1 day time land surface temperature (EarthData)

lst_mod11a2.daytime.trend_{..}: Deseasonalized monthly time-series (trend component of STL) for MOD13Q1 day time land surface temperature (EarthData)

lst_mod11a2.daytime.trend.ols.alpha_{..}: Alpha coefficient / intercept (derived by OLS) over the deseasonalized monthly time-series of MOD13Q1 day time land surface temperature (EarthData)

lst_mod11a2.daytime.trend.ols.beta_{..}: Beta coefficient / trend (derived by OLS) over the deseasonalized monthly time-series of MOD13Q1 day time land surface temperature (EarthData)

lst_mod11a2.nighttime_{..}: Monthly time-series of MOD13Q1 night time land surface temperature (EarthData)

lst_mod11a2.nighttime.{month}_{..}: Long-term monthly aggregation (2000—2022) for MOD13Q1 day time land surface temperature (EarthData)

lst_mod11a2.nighttime.trend_{..}: Deseasonalized monthly time-series (trend component of STL) for MOD13Q1 night time land surface temperature (EarthData)

lst_mod11a2.nighttime.trend.ols.alpha_{..}: Alpha coefficient / intercept (derived by OLS) over the deseasonalized monthly time-series of MOD13Q1 night time land surface temperature (EarthData)

lst_mod11a2.nighttime.trend.ols.beta_{..}: Beta coefficient / trend (derived by OLS) over the deseasonalized monthly time-series of MOD13Q1 night time land surface temperature (EarthData)

thermal_landsat.glad.ard_{..}: Quarterly time-series of Landsat thermal band (Witjes et al., 2023)

Climate layers:

accum.precipitation_chelsa.annual_{..}: Accumulated precipitation over the entire year according to CHELSA timeseries in mm of water (Karger et al., 2017)

accum.precipitation_chelsa.annual.3years.dif_{..}: 3-years difference considering the yearly accumulated precipitation according to CHELSA timeseries in mm of water (Karger et al., 2017)

accum.precipitation_chelsa.annual.log.csum_{..}: Cumulative sum, in logarithmic space, consdering the yearly accumulated precipitation according to CHELSA timeseries (Karger et al., 2017)

accum.precipitation_chelsa.montlhy_{..}: Accumulated precipitation for each month according to CHELSA timeseries in mm of water (Karger et al., 2017)

bioclim.var_chelsa.{variable_code}_{..}: Bioclimatic variables derived variables from the monthly mean, max, mean temperature, and mean precipitation values. For variable_code descriptions see chelsa-climate.org (Karger et al., 2017)

Accessibility & distance maps:

accessibility.to.ports_map.ox.{variable_code}_{..}: Time-required to access ports of different size according to Nelson et al., 2019

burned.area.distance_global.fire.atlas_{..}: Distance to burned areas mapped by Global Fire Atlas

cost.distance.to.coast_gedi.grass.gis_{..}: Cumulative cost of moving (derived by r.cost) to the coast

road.distance_osm.highways.high.density_{..}: Distance to high density of roads according to OpenStreetMap

road.distance_osm.highways.low.density_{..}: Distance to low density of roads according to OpenStreetMap

water.distance_glad.interanual.dynamic.classes_{..}: Distance to permanent / seasonal water bodies according to Pickens et al., 2020

Digital terrain model (DTM):

elev.lowestmode_gedi.eml_{..}: Mean estimate of the terrain elevation in dm filtered using SAGA GIS Gaussian filter (Witjes et al., 2023)

slope.percent_gedi.eml_{..}: Mean slope in % derived from terrain elevation ([Witjes et al., 2023]

Other existing maps:

pop.count_ghs.jrc_{..}: Annual time-series of population count in number of people mapped by Schiavina et al., 2023

snow.duration_global.snowpack_{..}: Annual duration of snow occurrence mapped by Global SnowPack

Files

train.csv: Training set with 42,237 rows and 420 columns, including sample id (sample_id - index column), land cover code (land_cover), land cover label (land_cover_label), reference year (year) and 416 features / covariates

test.csv: Test set with 42,271 rows and 418 columns, including sample id (sample_id - index column), reference year (year) and 416 features / covariates

sample_submission.csv: a sample submission file with 42,271 rows and 2 columns, including sample id (sample_id - index column) and predicted land cover code (land_cover)

All 311 Service Requests from 2010 to present. This information is automatically updated daily.

Energy Hackathon Event Market Outlook

According to our latest research, the global Energy Hackathon Event market size reached USD 1.14 billion in 2024, with a robust year-on-year growth trajectory. The market is expected to expand at a CAGR of 13.9% during the forecast period, projecting the market to attain a value of USD 3.28 billion by 2033. This remarkable growth is primarily driven by the increasing emphasis on sustainable energy innovation, the proliferation of digital collaboration tools, and the need for rapid ideation to address complex energy challenges worldwide.

The Energy Hackathon Event market is experiencing significant momentum due to the urgent global demand for sustainable energy solutions and the transition toward decarbonized energy systems. Governments, corporations, and non-profit organizations are leveraging hackathons as a strategic tool to crowdsource innovative ideas and accelerate the development of disruptive technologies in renewable energy, energy efficiency, and smart grids. The format of hackathons, which encourages rapid prototyping and collaborative problem-solving, aligns well with the fast-paced nature of energy transition initiatives. Furthermore, the increasing involvement of tech-savvy youth, startups, and professionals from diverse backgrounds is injecting fresh perspectives and fostering a culture of open innovation, which is essential for addressing the multifaceted challenges of the energy sector.

Another key growth factor for the Energy Hackathon Event market is the adoption of hybrid and virtual event formats, which have exponentially expanded participation and accessibility. The COVID-19 pandemic served as a catalyst for digital transformation in the events industry, leading to the proliferation of virtual hackathons that transcend geographical barriers. This shift has enabled organizations to tap into a global talent pool and foster cross-border collaborations, thereby accelerating the pace of innovation in energy technologies. Additionally, the integration of advanced digital platforms, real-time collaboration tools, and AI-driven judging systems has enhanced the overall experience and effectiveness of hackathons, making them more appealing to both participants and sponsors.

Corporate engagement and public-private partnerships are playing a pivotal role in driving the growth of the Energy Hackathon Event market. Leading energy companies, technology giants, and government agencies are increasingly sponsoring hackathons to scout for innovative solutions, recruit top talent, and strengthen their brand positioning as sustainability leaders. These events are not only fostering a culture of entrepreneurship but also serving as incubators for early-stage startups and breakthrough technologies. Moreover, the growing emphasis on open innovation ecosystems and the alignment of hackathon themes with global sustainability goals, such as the United Nations Sustainable Development Goals (SDGs), are further amplifying the impact and relevance of energy hackathons in the broader energy innovation landscape.

Regionally, North America and Europe continue to dominate the Energy Hackathon Event market due to their mature energy sectors, robust innovation ecosystems, and strong policy support for renewable energy and sustainability. However, Asia Pacific is emerging as a high-growth region, driven by rapid urbanization, rising energy demand, and increasing investments in clean energy technologies. The Middle East & Africa and Latin America are also witnessing growing interest in energy hackathons, particularly in the context of energy access, grid modernization, and climate resilience. This regional diversification is expected to further fuel the global expansion of the market in the coming years.

Event Type Analysis

The Event Type segment of the Energy Hackathon Event market is categorized into Physical, Virtual, and Hybrid formats, each presenting unique advantages and challenges. Physical hackathons have traditionally been the p

MassDOT Visualizing Transportation Hackathon, December 2013. Informing the Future of Massachusetts Transportation through Data Analysis and Visualization. Introduction At the MassDOT Visualizing Transportation Hackathon, the Massachusetts Department of Transportation (MassDOT), in partnership with the Mass Big Data Initiative, will release a series of related data sets on travel in Massachusetts and will open a challenge to the public to collaborate around analyzing this data and visualizing resulting insights to help inform the future of transportation in the Commonwealth. We invite participants to explore a collection of transportation data with a specific focus on travel behavior, road-rail comparisons, and the energy, environmental, and social impacts of transportation mode-choice. Background Each day in Massachusetts, travelers throughout the state make individual decisions on how to reach their destinations. Together, the public?s transportation ?mode choice? translates into sign

This website archive contains (in html and pdf format) copies of the content from the mpsfair.crc.nd.edu website established for The FAIR Hackathon Workshop for Mathematics and the Physical Sciences (MPS) held February 27-28, 2019 in Alexandria, Virginia. The workshop brought together forty-four stakeholders in the physical sciences community to share skills, tools and techniques to FAIRify research data. As one of the first efforts of its kind in the US, the workshop offered participants a way to engage with FAIR principles (Findable, Accessible, Interoperable and Reusable) Data and metrics in the context of a hackathon. The mpsfair.crc.nd.edu website is archived on the wayback machine at: https://web.archive.org/web/20210520211111/https://mpsfair.crc.nd.edu/ The MPS FAIR hackathon resources are still available as a publicly accessible project on the open science framework at: https://osf.io/km8db/ (DOI 10.17605/OSF.IO/KM8DB) The FAIR reading list featured on the former mpsfair.crc.nd.edu website is still available as a publicly accessible bibliography on zotero at: https://www.zotero.org/groups/2189857/mpsfair/library The mpsfair.crc.nd.edu worskhop website and its material are based upon work supported by the National Science Foundation under Grant Number 1839030. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Energy Hackathon Platform Market Outlook

According to our latest research, the global Energy Hackathon Platform market size reached USD 1.12 billion in 2024, and is projected to grow at a robust CAGR of 15.4% from 2025 to 2033. By the end of the forecast period in 2033, the market is expected to achieve a value of USD 3.74 billion. The primary growth driver for this market is the increasing demand for innovative and collaborative solutions to address complex energy sector challenges, fueled by the global push for renewable energy adoption, digital transformation, and sustainability initiatives.

The Energy Hackathon Platform market is witnessing remarkable growth due to the rising need for rapid innovation within the energy sector. As energy systems become more complex and interconnected, traditional R&D cycles are proving too slow to keep pace with the evolving landscape. Hackathons provide a dynamic and agile environment for ideation, prototyping, and solution development, enabling organizations to tap into diverse talent pools and crowdsource disruptive ideas. The proliferation of digital technologies and open-source tools has further lowered the barriers to participation, making hackathons an increasingly attractive option for corporates, startups, and public sector entities seeking to accelerate the development and deployment of new energy technologies.

Another significant growth factor is the escalating investments in renewable energy and smart grid infrastructure globally. Governments and private players alike are under pressure to meet ambitious sustainability targets, enhance energy efficiency, and ensure grid reliability. Energy hackathon platforms are emerging as critical enablers for these goals, providing structured frameworks for collaborative problem-solving and fostering partnerships between stakeholders from academia, industry, and government. These platforms not only catalyze the generation of innovative solutions but also play a pivotal role in talent identification, skills development, and ecosystem building, which are essential for the long-term transformation of the energy sector.

Furthermore, the increasing digitalization and data-driven nature of the energy industry are fueling the adoption of hackathon platforms. The integration of AI, IoT, cloud computing, and big data analytics into energy systems has created new opportunities and challenges that require rapid, multidisciplinary approaches to innovation. Hackathon platforms facilitate the convergence of expertise from software development, engineering, data science, and energy management, resulting in more holistic and impactful solutions. As organizations seek to leverage digital technologies to optimize operations, reduce costs, and enhance customer engagement, the demand for energy hackathon platforms is expected to intensify in the coming years.

Regionally, North America currently leads the Energy Hackathon Platform market, accounting for the largest share in 2024, driven by a mature technology ecosystem, strong corporate participation, and substantial government funding for energy innovation. Europe follows closely, benefiting from progressive energy policies and a vibrant research and startup community. The Asia Pacific region is poised for the fastest growth during the forecast period, propelled by rapid urbanization, increasing energy demand, and supportive government initiatives. Latin America and the Middle East & Africa are also witnessing growing interest, particularly in the context of renewable energy and grid modernization efforts, although market maturity and adoption rates vary significantly across countries.

Component Analysis

The Energy Hackathon Platform market is segmented by component into Platform and Services. The platform segment encompasses the core digital infrastructure that enables the organization, management, and execution of hackathons, including features such as team formation, challenge management, submission portals, judging s

Can you help #hackCambridge to tackle the city’s challenges?

Cambridge’s brainpower is being challenged to take part in a 24hr Hackathon to find ways of using technology to tackle the pressures facing the city as it grows.

Plans for over 33,000 houses to be built over the next 15 years will see an additional 50,000 people move into Cambridge and the surrounding area presenting an unprecedented challenge for the city.

Teams of hackers will work through the night using their tech skills and the city’s data to develop new ways to address the challenges to mobility, environment, heath and social care. The next morning they’ll pitch their ideas to a panel of experts who will award prizes for the best solutions.

The hackathon is part of #hackCambridge - a day of talks, workshops and activities being held at The Junction, Cambridge from 12 noon on Saturday 31 October to explore how technology and creative thinking can help to improve city life.

And it’s not just for techies – local residents can get involved through a workshop for non-techies, meet their mobile phone Data Shadow commissioned by Collusion and developed by multi-media artist Mark Farid, see Cambridge built in Minecraft and short films about the city’s inventors.

Data from Nationmaster.