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243 results for “Economy”
The MAT_STOCKS database: economy-wide material flows and material stock dynamics around the world
<p>Material stocks of buildings, infrastructure, machinery and other short-lived products form the biophysical basis of production and consumption. They are a crucial lever for resource efficiency and a sustainable circular economy, and for climate change mitigation. Here, we provide a global, country-level database of national-level material stocks differentiated by four end-uses and four summary material groups, for 177 countries from 1900 to 2016.</p> <p>This MAT_STOCKS database is derived from the economy-wide, dynamic, inflow-driven stock-flow model of Material Inputs, Stocks and Outputs (<em>MISO2) </em>(Wiedenhofer et al. 2024)<em>. </em>MISO2 covers 14 supply chain processes from raw material extraction to processing, trade, recycling and waste management, as well as 13 end-use types of stocks. Further information on the model and its system definition, as well as the model input data and assumptions and data processing procedures can be found in the accompanying peer-reviewed publication. The model code and exemplary input data can be found in the GitHub repository. </p> <p><strong>The MAT_STOCKS database version 1.0 </strong>provided here is summarized from the more detailed modeling presented in (Wiedenhofer et al. 2024). The dataset here gives:</p> <ul> <li>Material stocks by 4 main end-uses: buildings, infrastructure, machinery and other short-lived products (summarized from 13 detailed end-uses modeled) (S_10)</li> <li>Material stocks and flows by 4 main material groupings: biomass, non-metallic minerals, metals, as well as fossil-fuels derived materials (summarized from 23 raw materials and 20 stock-building materials modeled)</li> <li>Flows: Gross Additions to Stocks (F_9_10) and End-of-Life/Waste potentials (F_10_11)</li> <li>177 countries</li> <li>1900 to 2016 </li> </ul> <p>All units in kilotons. Paramter names are in accordance with the system definition given in the publication.</p> <p>Additionally, this repository includes all data presented in the figures of the related journal article.</p> <p><strong>Further information</strong></p> <p>This dataset complements the following scientific article:</p> <p>Wiedenhofer, Dominik and Streeck, Jan and Wieland, Hanspeter and Grammer, Benedikt and Baumgart, Andre and Plank, Barbara and Helbig, Christoph and Pauliuk, Stefan and Haberl, Helmut and Krausmann, Fridolin, From Extraction to End-uses and Waste Management: Modelling Economy-wide Material Cycles and Stock Dynamics Around the World (2024). Journal of Industrial Ecology, <a href="https://doi.org/10.1111/jiec.13575">https://doi.org/10.1111/jiec.13575</a></p> <p>The model code and its documentation are available on Github and Zenodo (see links below). For further information please see the publications. You can also contact Dominik Wiedenhofer <a href="mailto:dominik.wiedenhofer@boku.ac.at">dominik.wiedenhofer(a)boku.ac.at</a> and visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project: <em>MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</em></p> <p><strong>Funding</strong></p> <p>This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950), and the European Union's Horizon Europe programme (CircEUlar, grant agreement No 101056810). Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or granting authorities.<br><br></p>
Data for: The circular economy potential of urban organic waste streams in low- and middle-income countries
<p>This dataset includes the research data and supporting information for the publication "The circular economy potential of urban organic waste streams in low- and middle-income countries" which was published in the Journal of Environment, Development and Sustainability (DOI: 10.1007/s10668-021-01487-w).</p> <p>This dataset and the associated publication are the basis upon which the REVAMP (Resource Value Mapping) tool has been developed. See more info about the REVAMP tool here: https://www.sei.org/revamp</p> <p> </p>
Data on the Digital Economy and Society Index (DESI), the ASEAN Digital Integration Index (ADII), and the Digital Intelligence Index (DII)
<p>This dataset contains the quantitative measurement of the Digital Economy and Society Index (DESI), the ASEAN Digital Integration Index (ADII), and the Digital Intelligence Index (DII) in 2019.</p>
Database of permacultural adoption responses in Mexicali, BC, Mexico. based on Circular Economy, Knowledge Management, and Sustainability policies
<p>Database documenting the perspectives of citizens in Mexicali, Baja California, Mexico, regarding the adoption of permaculture practices. The study is analyzed through the lenses of Knowledge Management, Circular Economy, and Sustainability Policies. The data was collected during the summer of 2024. </p>
Gig economy in Poland
<p>This repository contains four datasets about the number of active users of selected mobile apps purchased from Selectivv company (https://selectivv.com/). Details regarding the data may be found below:</p> <p>How data was collected: Selectivv uses programmatic advertisements systems that collect information on about 24 mln smartphone users in Poland</p> <p><strong>Apps</strong>: </p> <ul> <li>Transportation: Uber, Bolt Driver, FREE NOW, iTaxi, </li> <li>Delivery: Glover, Takeaway, Bolt Courier, Wolt; </li> </ul> <p><strong>Unit</strong>: an active user of a given app. Active = used given app at least 1 minute in a given period (e.g. 1 unit during whole month, half-year).</p> <p><strong>Period</strong>: 2018-2018; monthly and half-year data</p> <p><strong>Spatial</strong> <strong>aggregation</strong>: country level, city level, functional area level, voivodeship level. Functional area is defined as here https://stat.gov.pl/en/regional-statistics/regional-surveys/urban-audit/larger-urban-zones-luz/ </p> <p><strong>Activity time</strong>: measured by activity time of given app (in hours; average and standard deviation) </p> <p>Datasets:</p> <ol> <li><strong>gig-table1-monthly-counts-stats.csv</strong> -- the monthly number of active users;</li> <li><strong>gig-table2-halfyear-demo-stats.csv</strong> -- the half-year number of active users by socio-demographic variables;</li> <li><strong>gig-table3-halfyear-region-stats.csv</strong> -- the half-year number of active users by spatial aggregation;</li> <li><strong>gig-table4-halfyear-activity-stats.csv</strong> -- the half-year activity time by working week, weekend, day (8-18) and night (18-8).</li> </ol> <p>Detailed description: </p> <p><strong>1. gig-table1-monthly-counts-stats.csv</strong></p> <p>Structure: </p> <ul> <li>month - YYYY-MM-DD -- we set all dates to 15th of given month but actually the data is about the whole month (active users in whole period); 2018-01-15 to 2021-12-15</li> <li>app -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)</li> <li>number_of_users -- the number of active users</li> <li>category -- Transportation, Deliver</li> </ul> <p><strong>2. gig-table2-halfyear-demo-stats.csv </strong></p> <p>Structure: </p> <ul> <li>gender -- men, women</li> <li>age -- 18-30, 31-50, 51-64</li> <li>country -- Poland, Ukraine, Other</li> <li>period -- 2018.1, 2018.2, 2019.1, 2019.2, 2020.1, 2021.2</li> <li>apps -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)</li> <li>number_of_users -- the number of active users</li> <li>students -- the share of students within a given row</li> <li>parents_of_children_0_4_years -- the share of parents of 0-4 years children in a given row</li> <li>parents_of_children_5_10_years -- the share of parents of 5-10 years children in a given row</li> <li>women_planning_a_baby -- the share of women planing a baby in a given row</li> <li>standard -- the share of standard smartphones in a given row</li> <li>premium_i_phone -- the share of iPhone smartphones in a given row</li> <li>other_premium -- the share of other premium smartphones in a given row</li> <li>category -- Transportation, Delivery</li> </ul> <p><strong>3. gig-table3-halfyear-region-stats.csv</strong></p> <p>Structure:</p> <ul> <li>group -- Voivodeship, Functional Area, Cities</li> <li>period -- 2018.1, 2018.2, 2019.1, 2019.2, 2020.1, 2021.2</li> <li>region_name:</li> <li>Cities -- Białystok, Bydgoszcz, Gdańsk, Gdynia, Gorzów Wielkopolski, Katowice, Kielce, Kraków, Łódź, Lublin, Olsztyn, Opole, Poznań, Rzeszów, Sopot, Szczecin, Toruń, Warszawa, Wrocław, Zielona Góra</li> <li>Functional Area -- Functional area - Białystok, Functional area - Bydgoszcz, Functional area - Gorzów Wielkopolski, Functional area - GZM, Functional area - GZM2, Functional area - Kielce, Functional area - Kraków, Functional area - Łódź, Functional area - Lublin, Functional area - Olsztyn, Functional area - Opole, Functional area - Poznań, Functional area - Rzeszów, Functional area - Szczecin, Functional area - Toruń, Functional area - Trójmiasto, Functional area - Warszawa, Functional area - Wrocław, Functional area - Zielona Góra</li> <li>Voivodeship -- dolnośląskie, kujawsko-pomorskie, łódzkie, lubelskie, lubuskie, małopolskie, mazowieckie, opolskie, podkarpackie, podlaskie, pomorskie, śląskie, świętokrzyskie, warmińsko-mazurskie, wielkopolskie, zachodniopomorskie</li> <li>apps -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)</li> <li>number_of_users -- the number of active users</li> <li>category -- Transportation, Delivery</li> </ul> <p>Please note that:</p> <ul> <li>the number of active users in a given functional area = number of active users in a city and a functional area of this city</li> <li>the number of active users in voivodeship = number of active users in a city, its functional area and the rest of the voivodeship where this city and functional area is located </li> </ul> <p>More details here: https://stat.gov.pl/en/regional-statistics/regional-surveys/urban-audit/larger-urban-zones-luz/ </p> <p><strong>4. gig-table4-halfyear-activity-stats.csv</strong></p> <p>Structure:</p> <ul> <li>period -- 2018.1, 2018.2, 2019.1, 2019.2, 2020.1, 2021.2</li> <li>apps -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)</li> <li>day -- Mondays-Thursdays, Fridays-Sundays</li> <li>hour -- day (8-18), night (18-8)</li> <li>activity_time -- in hours </li> <li>statistic -- Average, Std.Dev. (standard deviation)</li> <li>category -- Transportation, Delivery</li> </ul>
Figure data and model used in Stranded fossil-fuel assets translate to major losses for investors in advanced economies
<p>The package contains i) the figure code and underlying data to create all figures in the main paper and supplementary information of the journal article and ii) the network and imputation model used to calculate the shock calculation.</p>
The TREASURE semantic social network data on the circular economy aspect of automotive manufacturing
<p>The <a href="https://www.treasureproject.eu/">TREASURE</a> project looks at industrial innovation to address the problem of making onboard electronics in the automotive industry easier to recycle, increasing the industry's contribution to the circular economy. As part of it, a team of ethnographers generated and coded the corpus contained in this dataset. interviews conducted between January 2022 and June 2023 with car owners and enthusiasts at car industry events. The interviews focus on experiences with car electronics and perspectives on sustainability and the circular economy. The dataset is pseudonymized. TREASURE is supported by the European Union's Horizon 2020 programme, grant n. 101003587.</p>
Global scientific research commons under the Nagoya Protocol: Towards a collaborative economy model for the sharing of basic research assets
<p>This paper aims to get a better understanding of the motivational and transaction cost features of<br /> building global scientific research commons, with a view to contributing to the debate on the design of<br /> appropriate policy measures under the recently adopted Nagoya Protocol. For this purpose, the paper<br /> analyses the results of a world-wide survey of managers and users of microbial culture collections, which<br /> focused on the role of social and internalized motivations, organizational networks and external<br /> incentives in promoting the public availability of upstream research assets. Overall, the study confirms<br /> the hypotheses of the social production model of information and shareable goods, but it also shows the<br /> need to complete this model. For the sharing of materials, the underlying collaborative economy in<br /> excess capacity plays a key role in addition to the social production, while for data, competitive pressures<br /> amongst scientists tend to play a bigger role.</p>
Linear and circular economy figure
<p>Two figures representing a linear and circular economy approach with a plastic bottle as an example but is applicable to any recycable waste product and especially single-use plastic packaging.</p> <p>In the linear economy model the product is manufactured using principally new resources, largely petroleum based. Most of the product’s value is lost during its life cycle because of leakage along the entire value chain (red arrows), including pellet loss, littering, combined sewage overflow, loss during transport and improper storage of waste, and poorly designed products that are easily lost to the environment and difficult to recover (microbeads, small wrappers, torn corners of packaging). This leads to a contamination of the environment, affecting wildlife and human well-being. A small proportion is recycled (green arrow) for remanufacture, with the remainder utilized for energy recovery.</p> <p>In the circular economy model a high percentage of recycled content is used as feedstock for new products, and the remainder from sustainable sources (potentially biopolymers). Poor practices (red arrows) throughout the life cycle are mitigated, for example, by proper legislative policy, public awareness that leads to proper consumer waste handling, and incentivized recovery systems (e.g., returnable bottles). Recovery is further improved by regulating end-of-life design in products and packaging. This leads to reduced leakage of plastic to the environment from all sectors of society, and significant improvements are social justice concerns for communities that manage waste. The small amount of residual plastic is then disposed of responsibly.</p> <p> </p> <p>Figure and figure captions originally published in:</p> <p>Eriksen M, Thiel M, Prindiville M, Kiessling T 2018. Microplastic: what are the solutions? in Freshwater Microplastics - Emerging Environmental Contaminants (Eds Wagner M, Lambert S), The Handbook of Environmental Chemistry 58. Springer.</p> <p>Licence: Creative Commons 4.0 BY. Feel free to reuse, to change, and to adapt - just refer to the above-mentioned publication. The .svg file allows you to change parts of the figures easily with Inkscape (a free vector graphic program).</p>
Data set: Variations in water economy traits in two Sphagnum species across their distribution boundaries
<p><em>Sphagnum</em> trait data collected (2016-2017) across a climatic gradient in Sweden. Trait data for both shoot and canopy traits. Data for <em>Sphagnum cuspidatum</em> and <em>Sphagnum lindbergii</em>. Also contains data on species occurrence records in Sweden and output from speceis distribution modelling. See published paper for more information.</p> <p>Files contain (i) processed data ("calculated_trait_data...cvs"), (ii) raw data ("Campbell_etal_clim_traits_...cvs"), (iii) their readme files, and (iv) R-scripts to run the analyses. Note that you need the files in the zip-file to run the analyses in the R-script. The zip-file contains all raw data (climate, traits, species occurences), MaxEnt output, and raster files from photogrammetry.</p> <p>More info in paper: <a href="https://doi.org/10.1002/ajb2.16347" target="_blank" rel="noopener">https://doi.org/10.1002/ajb2.16347</a></p>
Research Data/Code for "Scale-bridging within a complex model hierarchy for investigation of a metal-fueled circular energy economy by use of Bayesian model calibration with model error quantification"
<p>This repository contains research data and code for supplementing the manuscript <br>"Scale-bridging within a complex model hierarchy for investigation of a metal-fueled circular energy economy by use of Bayesian model calibration with model error quantification" <br>by L. Gossel, E. Corbean, S. Dübal, P. Brand, M. Fricke, H. Nicolai, C. Hasse, S. Hartl, S. Ulbrich, and D. Bothe. </p> <p>There is a corresponding preprint available on Arxiv: https://doi.org/10.48550/arXiv.2404.13092</p> <p><br>Users are referred to the manuscript for background information. This repository shall enable reproduction of the reported results and does not stand alone. </p> <p>Please read important information in the README in the top-level directory. </p> <p>Funded by the Hessian Ministry of Higher Education, Research, Science and the Arts - cluster project Clean Circles. </p>
Barriers to Transitioning to a Circular Bio-based Economy: Findings from an Industrial Perspective
<p>The transition from a linear fossil-based to a circular bio-based economy represents an opportunity and a suitable pathway for achieving several sustainable development goals. However, the transition is a complex process since it requires transformative policies, purposeful innovation, access to finance, risk-taking capacity as well as new and sustainable business models and markets. Accordingly, the first step in this transition process is the identification of barriers that are hampering the transition to a sustainable circular bio-based economy. With this motivation in mind, this study reviews grey literature to identify barriers focusing on four critical sectors facing major challenges within the current linear economy and requiring a sustainable transition most urgently: construction, chemicals, plastics, and textile sectors. Employing an adapted STEEP methodology (Social, Technological, Economical, Environmental, Political), a total of 193 different barriers have been identified and clustered under six categories: cultural, technical, economic, environmental, governance, and structural. Regardless of the sector, cultural and structural barriers are identified as the most prominent; the lack of incentives for consumer behaviour change and lack of stakeholder collaboration were the most cited barriers among the literature records. From a value chain perspective, most of the barriers are related to the material processing and product manufacturing stage. Finally, potential solutions, extracted from the grey literature, are proposed to fill the gaps and overcome the identified barriers. Many of the identified barriers are common across the four investigated sectors, indicating the solutions or measures can be applicable in a wider perspective to promote the transition in the right direction.</p>
Survey of Electrical & Electronic Equipment (EEE) Sector Stakeholders & End Users of EEE regarding Circular Economy in the EEE Sector
<p>The data is the full results of surveys of stakeholders in the electrical and electronic equipment (EEE) value chain conducted as part of the Horizon 2020 funded <a href="https://c-serveesproject.eu/index.php">C-SERVEES project</a>. </p> <p>The purpose of the survey was to provide guidance for the successful implementation of circularity in the EEE sector, specifically to develop circular economy business plans for four items of equipment: printers (inc. toner cartridges); washing machines; televisions; and network monitoring equipment. The survey aimed to capture data on:</p> <ul> <li>Circular economy awareness;</li> <li>Current practices relating to circular economy; and</li> <li>Circular economy opportunities, barriers and enablers.</li> </ul> <p>Investigation of opportunities, barriers and enablers focused on technical, economic, socio-cultural, regulatory and environmental themes. In addition, background information was also collected to enable some of the attributes of the respondent to be recorded. This included: country; city, years of experience in the industry as well as, in the household users’ survey, age and education level. </p> <p>The stakeholder groups surveyed are noted below, these being the key EEE supply chain actors. A separate dataset is available for each.</p> <ol> <li>Designers of EEE</li> <li>Suppliers to manufacturers of EEE</li> <li>Manufacturers of EEE</li> <li>EEE Retailers</li> <li>Business users of EEE</li> <li>Household users of EEE</li> <li>Waste EEE handlers</li> </ol> <p>A project report analysing the results of the survey is available: <em><a href="https://c-serveesproject.eu/project_activities.php?op=5">Guidelines for circular economic models in the E&E sector</a> . </em>A journal article outlining the subsequent development of the circular economy business models is also available: <a href="https://www.sciencedirect.com/science/article/abs/pii/S095965262101430X"><em>A circular economy business model innovation process for the electrical and electronic equipment sector</em></a>. </p>
SM: Economy-wide impacts of socio-politically driven net-zero energy in Europe
<p><strong>Supplementary Material (SM): Economy-wide impacts of socio-politically driven net-zero energy in Europe</strong></p> <p>Two zipped folders</p> <p>(a) Euro-Calliope.zip includes</p> <p>-Energy system configurations by storyline (market-driven, government-directed, people-powered) and year (2030, 2050).</p> <p>(b) WEGDYN.zip includes</p> <p>-Supplementary Material (SM_Regionaleconomiceffects.pdf)<br>-Processed Euro-Calliope output data to WEGDYN input data (EC2WD_data.xlsx)<br>-WEGDYN results (WEGDYN_data.xlsx)<br>-WEGDYN resolution, nesting trees, elasticities (WEGDYN_model.xlsx)</p>
SI2: How circular is an extractive economy? South Africa's export orientation results in low circularity and insufficient societal stocks for service-provisioning
<p>Supporting information SI2 for the manuscript under review:</p> <p>How circular is an extractive economy? South Africa’s export orientation results in low circularity and insufficient societal stocks for service-provisioning </p> <p> </p> <p>It provides the data used and the basic mass balanced calculation for a circularity assessment.</p>
Nicola Armaroli - Towards a Circular Economy
<p>What does 'circular economy' stand for? How can we incorporate it in our daily lives?</p> <p>Learn more about this key concept & don't miss our interview with Nicola Armaroli, research director at the National Research Council of Italy and a SUNRISE consortium member</p>
Dataset for the paper "Ripa M, Di Felice LJ, Giampietro M (2021). The energy metabolism of post-industrial economies. A framework to account for externalization across scales. Energy, 214." https://doi.org/10.1016/j.energy.2020.118943
<p>This repository contains the data needed to reproduce the results in: </p> <p>Ripa M, Di Felice LJ, Giampietro M (2021). The energy metabolism of post-industrial economies. A framework to account for externalization across scales. Energy, 214." https://doi.org/10.1016/j.energy.2020.118943</p> <p>The dataset was also used for a case study in "Di Felice L., Dunlop T., Giampietro M., Kovacic Z., Renner A., Ripa M., Velasco-Fernández R. – Report on the Quality Check of the Robustness of the Narrative behind Energy Directives. MAGIC (H2020–GA 689669) Project Deliverable 5.4, 30 November 2018". (link: https://magic-nexus.eu/documents/d54-report-narratives-behind-energy-directives).</p> <p>Sources of data are specified in the dataset (under tab "References")</p>
Data for the submitted paper by Yasunari et al., "Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments"
<p>The dataset contains some of the outputs from the global climate model experiments by MIROC5 on changing Siberian wildfire severities, the other data used in the paper (see READ_ME files on the data sources), the analyzed data, and the scripts for analyses, which were used in the following submitted paper. Note that this dataset also includes unused data for the paper:</p> <p><br>Yasunari, T. J., D. Narita, T. Takemura, S. Wakabayashi, and A. Takeshima, Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments, submitted.</p> <p>Please read the READ_ME files for detailed information in each directory (especially see the "about_figures_and_tables/" directory first). Because of their large sizes, the data were separated into three zipped files.</p>
Model output from 'Modelling the circular economy potential of micromobility: A Finnish case study for e-scooters and e-bikes'
<p>These two files include the results for e-scooters and e-bikes. The dynamics of the BAU scenario is shown in the first few columns, then follow the sensitivity analyses. The column names should be self-explanatory, subject to reading the paper.</p>
Economies of scale shape energetics of solitary and group living spiders and their webs
<p>Metabolic scaling, whereby larger individuals use less energy per unit mass than smaller ones, may apply to the combined metabolic rate of group-living organisms as group size increases. Spiders that form groups in high disturbance environments can serve to test the hypothesis that economies of scale benefit social groups.</p> <p>Using solitary and group-living spiders, we tested the hypothesis that spiders exhibit negative allometry between body or colony mass and the standing mass of their webs and whether, and how, such a relationship may contribute to group-living benefits in a cooperative spider.</p> <p>Given the diverse architecture of spider webs—orb, tangle, and sheet-and-tangle, and associated differences in silk content, we first assessed how standing web mass scales with spider mass as a function of web architecture and whether investment in silk differs among web types. As group-living spiders are predominantly found in clades that build the presumably costlier sheet-and-tangle webs, we then asked whether cost-sharing through cooperative web maintenance contributes to a positive energy budget in a social species.</p> <p>We found that larger spiders had a relatively smaller investment in silk per unit mass than smaller ones, but more complex sheet-and-tangle webs contained orders of magnitude more silk than simpler orb or tangle ones. In the group-living species, standing web mass per unit spider mass continued to decline as colony size increased with a similar slope as for unitary spiders. When web maintenance activities were considered, colonies also experienced reduced mass-specific energy expenditure with increasing colony size. Activity savings contributed to a net positive energy balance for medium and large colonies after inputs from the cooperative capture of large prey were accounted for.</p> <p>Economies of scale have been previously demonstrated in animal societies characterized by reproductive and worker castes, but not in relatively egalitarian societies as those of social spiders. Our findings illustrate the universality of scaling laws and how economies of scale may contribute to setting limits on social behaviour and hunting strategies.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.